Comparative analysis of GIS and RS based models for delineation of groundwater potential zone mapping
Comparative analysis of GIS and RS based models for delineation of groundwater potential zone...
Islam, Fakhrul; Tariq, Aqil; Guluzade, Rufat; Zhao, Na; Shah, Safeer Ullah; Ullah, Matee; Hussain, Mian Luqman; Ahmad, Muhammad Nasar; Alasmari, Abdulrahman; Alzuaibr, Fahad M.; Askary, Ahmad El; Aslam, Muhammad
2023-12-31 00:00:00
GEOMATICS, NATURAL HAZARDS AND RISK 2023, VOL. 14, NO. 1, 2216852 https://doi.org/10.1080/19475705.2023.2216852 Comparative analysis of GIS and RS based models for delineation of groundwater potential zone mapping a b,c d e f Fakhrul Islam , Aqil Tariq , Rufat Guluzade , Na Zhao , Safeer Ullah Shah , g h c Matee Ullah , Mian Luqman Hussain , Muhammad Nasar Ahmad , Abdulrahman i i j k Alasmari , Fahad M. Alzuaibr , Ahmad El Askary and Muhammad Aslam a b Department of Geology, Khushal Khan Khattak University, Karak, Pakistan; Department of Wildlife, Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, MS, USA; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China; School of Earth Science and Engineering, majoring in Geodesy and Survey Engineering, Hohai University, Nanjing, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Board of Revenue, Government of Pakistan, Peshawar, KPK, Pakistan; Faculty of Earth sciences, Geography and Astronomy, University of Vienna, Vienna, Austria; National Centre of Excellence in Geology, University of Peshawar, Peshawar, KPK, Pakistan; Department of Biology, Faculty of Science, University of Tabuk, Tabuk, Saudi Arabia; Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia; School of Computing Engineering and Physical Sciences, University of West of Scotland, Paisley, UK ABSTRACT ARTICLE HISTORY Received 15 February 2023 Groundwater is a crucial natural resource that varies in quality and Accepted 17 May 2023 quantity across Khyber Pakhtunkhwa (KPK), Pakistan. Increased popu- lation and urbanization place enormous demands on groundwater KEYWORDS supplies, reducing both their quality and quantity. This research aimed Ground water potential to delineate the groundwater potential zone in the Kohat region, zones; GIS; RS; FR; AUC Pakistan by integrating twelve thematic layers. In the current research, Groundwater Potential Zone (GWPZ) were created by implementing Weight of Evidence (WOE), Frequency Ratio (FR), and Information Value (IV) models of the Kohat region. In this study, we used Sentinel- 2 satellite data were utilized to generate an inventory map of ground- water using machine learning algorithms in Google Earth Engine (GEE). Furthermore, the validation was done with a field survey and ground data. The inventory data was divided into training (80%) and testing (20%) datasets. The WOE, FR, and IV models are applied to assess the relationship between inventory data and groundwater fac- tors to generate the GWPZ of the Kohat region. Finally, the current research results of Area Under Curve (AUC) technique for WOE, FR, and IV models were 88%, 91%, and 89%. The final GWPZ can aid in better future planning for groundwater exploration, management, and supply of water in the Kohat region. CONTACT Na Zhao zhaon@lries.ac.cn 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. 2 F. ISLAM ET AL. 1. Introduction Groundwater is an important natural resource that makes up about 34% of the world’s freshwater supply (Tariq, Siddiqui, et al. 2022). It is the primary water supply and is regarded as less contaminated than other water sources. It supplies approxi- mately half of the freshwater that can easily be accessed and used for cleaning, drink- ing, and cooking regularly (Termeh et al. 2019). Groundwater meets the requirements of 97% of the world’s population for freshwater and provides 50% of the world’s irri- gation (Tariq and Shu 2020). It can be considered the essential capital natural posses- sions that occurred in the sediments and fractures of soil and rock (Ahmad et al. 2020). Groundwater is commonly utilized for domestic, industrial, and farming pur- pose in numerous parts of the world (Mumtaz et al. 2023). The tremendous demand for groundwater is increasing rapidly, and this rising need for water frequently causes overutilization, which is striking massive stress on the inadequate groundwater source. Furthermore, freshwater matter has become a critical issue in the tropical and subtropical areas of the world due to unscientific irrigation, exploration, urbanization, and changes in climatic factors. Furthermore, these methods only sometimes account for the several parameters that influence groundwater’s existence, storage, and mobility in rocks and soil (Keesstra et al. 2012). While currently, with the advancement of computer technology, geospatial techniques have become the most effective, emerging, and innovative ways to delineate potential groundwater regions. These methods can be applied locally and regionally based using ground and satellite data. These ground and remote sensing (RS) data are processed in the geographic information system (GIS) platform to detect and categorize the influencing parameters of groundwater (Basharat et al. 2022). RS provides inexpensive data as input to the GIS platform for the regional and inaccessible regions with a temporal, spectral, and spatial resolution (Majeed et al. 2022; Sadiq Fareed et al. 2022; Tariq, Mumtaz, et al. 2023). Satellite data can compute geological information (fault, fold, fractures, lithology) and topographic, climatic, and hydrological parameters for groundwater assessment. Geospatial technology is an innovative science to collect, store, display, and analyze various ground and RS data for groundwater in the form of surface water inventory (e.g. dams, ponds, open wells, and springs), groundwater demarcation, surface water modelling, and groundwater contamination (Siddiqui et al. 2020; Zainab et al. 2021; Tariq, Yan, et al. 2022; Tariq, Jiango, Li, et al. 2023). In the past, numerous GIS and RS based models have been applied by scientist, i.e. Evidential Belief Function (EBF) (Shah et al. 2021), Weight of Evidence (WOE) (Lee, Kim, et al. 2012), Frequency Ratio (FR) (S. Hasan AL-Zuhairy et al. 2017), Decision Tree (DT), (Lee, Song, et al. 2012), Classification and Regression Tree (CART), (Mohammadi et al. 2021), Boosted Regression Tree (BRT), (Kordestani et al. 2019), Artificial Neural Network (ANN), (Lee, Song, et al. 2012), Multivariate Adaptive Regression Splines (MARS), (Kalantar et al. 2018), Binary Logistic Regression (BLR), (Chen et al. 2018), Analytic Hierarchy Process (AHP), (Singh et al. 2018), Random Forest (RF), (Tariq et al. 2021), Fuzzy Logic (FL), (Shahid et al. 2002), Support Vector Machine (SVM), (Lee et al. 2017), Multi-criteria Decision Analysis (MCDA) GEOMATICS, NATURAL HAZARDS AND RISK 3 (Kaliraj et al. 2014), Quadratic Discriminant Analysis (QDA) (Baloch et al. 2021), K- Nearest Neighbour (KNN) (Naghibi et al. 2018). The SVM is the technique to predict Groundwater Potential Zone (GWPZ) (Eid et al. 2023). Groundwater is most vital and significant natural resource for sustainability due to its agriculture-dependent economy in Pakistan. Groundwater is a vital element in the economy, but human population, industrialization, unscientific exploration, and groundwater mismanagement have twisted a chief risk to this treasured energy source (Moazzam et al. 2022). Therefore, GWPZ is an indispensable technique for mapping and managing the precious water resources in the area of interest (Baloch et al. 2021). Numerous field survey mechanisms, i.e. geological, geophysical, and hydro- logical studies, have been used by researchers to demarcate potential groundwater zones (Israil et al. 2006). These methods need several human resources financial budget, and it can be most time-consuming. In this research, WOE, FR, and IV models were utilized to locate the GWPZ in the KPK region of Pakistan. Although several studies have been conducted across Pakistan utilizing RS and GIS techniques to delineate the groundwater potential map, none of those studies have been conducted in this region where sustainable ground- water resources management is essential for the industrial, commercial development, and economy of the country. In the current study, we used twelve influencing param- eters that are considered significant to explore the deficient, low, medium, high, and very high potential groundwater regions. These parameters were prepared in the GIS platform from various ground and RS data. In the current research, three geospatial techniques were used to compute the association of influencing parameters with groundwater inventory data and to delineate potential groundwater regions in district Kohat, Pakistan. These GIS-based groundwater mapping models have not been inves- tigated previously in the district Kohat. The final GWPZ map can be helpful for deci- sion-makers to assess and manage groundwater in various regions of the study area. 2. Material and methodology 2.1. Study area The current research is conducted in district Kohat, situated in the southern part of Khyber Pakhtunkhwa (KPK), Pakistan. The Kohat region is geographically extended 0 00 0 00 0 00 0 00 from 33 35 13 33 49 73 N and 71 52 49 Eto 71 26 32 (Figure 1a–c) (Hussain 2014). The study area occurred at an elevation of about 2000 m. Climatically the research region is considered a limited steppe climate region with slight precipitation throughout the year. In Kohat District, the summers are long, hot, humid, and clear, while the winters are brief, cold, and mostly clear. Both seasons have clear skies. Temperatures below 0.5 C or above 43.33 C are extremely uncommon throughout the year. On average, the temperature ranges from 2.2 Cto 39.45 C (Azra et al. 2019). Geologically the current study area is situated in Kohat Plateau. The study area includes fold and thrust belt collections which are thin- skinned structures covered by thick-skinned structures. Compressional structures sub- ject a significant portion of the plateau; however, the strike-slip faulting is limited to the southern Kohat plateau (Hussain and Zhang 2018). The Kohat plateau is mainly 4 F. ISLAM ET AL. Figure 1. (a) Geographical location of Pakistan, (b) Provincial boundary of KPK where study area exists, and (c) Location map of study area with elevation. occupied by lithologies of Eocene limestone, shale, evaporates, and subordinate clays, and younger clastic sedimentary rocks of the Miocene–Pliocene age (Hussain et al. 2021). The age of sedimentary rocks in the plateau is composed of Paleocene to Pliocene, which was first deposited on the northern Indian plate margin (Tariq and Qin 2023). 2.2. Datasets Various datasets are applied to generate different parameters used in the current work. The datasets utilized in the current research comprise organization ground (field survey and ground data) and RS data. The ground and satellite information applied to prepare twelve influential parameters for groundwater potential were acquired from appropriate national and international research platforms. The data details and sources of information are mentioned in Table 1. 2.3. Methodology The study was established in four phases: i) preparation of ground water inventory map of the study area using different geospatial, machine learning, and field survey GEOMATICS, NATURAL HAZARDS AND RISK 5 Table 1. Description of datasets were used in this research. Data Scale/resolution Data availability Data availability statement/source Maps of parameters Sentinel 2 10 m Freely available The data supporting this study’s findings are openly available in [USGS] at LULC, NDVI Map and ground https://earthexplorer.usgs.gov. water inventory ALOS DEM 12.5m Freely available The data that support the findings of this study are openly Elevation, Slope, Aspect, Curvature, available at https://asf.alaska.edu and drainage CHIRPS 0.05 Freely available The data supporting this study’s findings are available in [UCSB] at Rainfall maps https://www.chc.ucsb.edu. The spatial resolution of CHIPRS is 0:05 (5.54 Km) and daily gridded. Soil 1: 2000000 Freely available le Freely available at Soil type of Pakistan and FAO Soil Texture Map Geology 1:650000 Geological Map Lithology and Fault map 6 F. ISLAM ET AL. Figure 2. Flowchart of present research work for the current research study. techniques, ii) generation of twelve influential groundwater parameters, iii) generating GWPZ using three geospatial models like WOE, FR, and IV and, vi) performing val- idation and accuracy assessment using AUC technique. The comprehensively organ- ized methodology for the present investigation is shown in Figure 2. 2.3.1. Inventory map of surface water bodies The accurate water inventory map is the primary and essential parameter to generate GWPZ for the region of interest. The ground and RS data for the inventory map were collected from various public organizations and satellite sources. The inventory map of different water bodies was prepared from Sentinel-2 using a ML-model. The inventory map was validated and verified with ground data collected from the public department of district Kohat and various field surveys in the Kohat region. Finally, detected inventory data of current research is divided into training (80%) and testing (20%) datasets (Zhu et al. 2022). 2.3.2. Preparation of GWPZ conditioning parameters Considering groundwater potential, conditioning parameters is a significant task affecting the final output map of GWPZ; hence, conditioning parameters should be cautiously designated (Bui et al. 2019). The existence and yield of groundwater in a specified aquifer are influenced by numerous parameters. In the present study, twelve influential conditioning factors like elevation, slope angle, aspect, curvature, drainage network, rainfall, LULC, soil, NDVI and the road distance are considered to evaluate the influences of mentioned parameters on groundwater potential in the study area. GEOMATICS, NATURAL HAZARDS AND RISK 7 Figure 3. Parameters for GWPZ; (a) elevation, (b) slope, (c) aspect, (d) curvature (e) drainage net- work (f) rainfall, (g) LULC, (h) lithology, (i) fault distance, (j) soil types, (k) NDVI (l) road distance. 2.3.2.1. Elevation. Altitude influences the potential groundwater zone as it is con- versely associated with the reservoir (Karimi-Rizvandi et al. 2021). The altitude of the present research area is computed from having 12.5 m spatial resolution and reclassi- fied into five categories in ArcMap 10.8 as revealed in Figure 3a. 2.3.2.2. Slope. The slope gradient is another significant parameter for groundwater potential because the slope angle directly influences the amount of rainwater water intrusion and surface run-off in any region. A steep slope gradient negatively impacts groundwater reservoirs because a higher slope enables a rapid run-off area and reduces water infiltration. In contrast, a low slope promotes water infiltration and potential recharge area (Maskooni et al. 2020). The slope of the Kohat region reclas- sify into five classes, i.e. <5 ,5–15 ,15–25 ,25–35 and >35 using ArcGIS 10.8 as shown in Figure 3b. The highest slope of the present research area is verified as 78 , while the lowest slope of the region is recorded as 0 . 2.3.2.3. Aspect. The slope aspect presents slope directions that affect the quantity of precipitation, radiation of the sun, wind speed, and LULC, which concomitantly strike the amount of water permeation to the pore spaces of sediments influencing groundwater potential in the region (Solomon and Quiel 2006). The aspect of the present area is generated and reclassified into nine classes, as revealed in Figure 3c. 8 F. ISLAM ET AL. 2.3.2.4. Curvature. The curvature map shows the association with the capacity to store and hold water reserves on the area of surface. Usually, the dipped structures accumulate more water bodies (Pham et al. 2019). The curvature of the Kohat region is calculated from ALOS DEM having 12.5 m resolution and reclassified into open, flat, and convex groups, as mentioned in Figure 3d. 2.3.2.5. Drainage network. Drainage network presents an inverse association with the percolation of water in fracture and sediments of strata because river density discour- ages water retention (Kordestani et al. 2019). As the river network density is high, water recharge in the area will be low and vice versa because river density favours surface runoff and decreases infiltration. The five buffers were applied to the stream network of the present research area stream, as shown in Figure 3e. 2.3.2.6. Rainfall. Climatic parameters are influential in controlling the water table from the seasonal perception. Precipitation is a significant climatic factor that affects groundwater recharge. Precipitation is considered a vital parameter for potential groundwater mapping because the probabilities of penetration are more in cases of high rainfall; consequently a chief source of water recharge in the area (da Silva Monteiro et al. 2022). The precipitation of the present study area is computed from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) of 2010 to 2022 using a ML Algorithm in GEE. The concluding precipitation map was then reclassified into five classes using GIS environment as shown in Figure 3f. 2.3.2.7. LULC. LULC is a significant parameter influencing groundwater recharge, occurrence, and availability. LULC presents environmental parameters having a sig- nificant impression on groundwater because they affect penetration and surface run- off (Bui et al. 2019). Moreover, bare ground and built-up regions usually display low potential, while vegetation and the area near water reservoirs illustrate higher ground- water potential. LULC map of the Kohat region is generated from Sentinel-2 data in GEE using a ML algorithm. This causative factor was further categorized into six classes for evaluating these classes on groundwater, as shown in Figure 3g. Confusion matrices were used to create classification accuracy processes, such as overall accuracy, omission and commission errors, and Cohen’s kappa statistic. These classification accuracy matrices were derived using confusion matrices (Firdaus 2014). As per reference data, the commission error is the percentage of pixels that have been incorrectly assigned to classes they do not belong. On the other hand, the per- centage of pixels that should have been assigned to a particular class according to the reference data but have not been assigned to that class is an omission error. It was figured out how to determine the omission and commission error for each LULC class and estimate the average for all classes. 2.3.2.8. Lithology. The lithology of strata controls the porosity and permeability of aquifers and influences groundwater due to its conductivity and penetration. These rock and soil properties affect groundwater’s existence, accumulation, and mobility GEOMATICS, NATURAL HAZARDS AND RISK 9 (Muavhi et al. 2022). The lithology of the Kohat region is extracted from the Northern Geological Map of Pakistan, as shown in Figure 3h. 2.3.2.9. Fault distance. The fault Buffer of various distance gaps was selected for ana- lysis because it influences the subsurface flow of fluids (Yin et al. 2018). Therefore, geological faults and fractures are critical parameters in detecting groundwater sour- ces. The fault parameter is digitized in this research, as shown in Figure 3i. Five buf- fers were applied to calculate the relationship of fault with groundwater potential in the study area. 2.3.2.10. Soil types. Soil is the uppermost horizon of land which helps in water infiltra- tion. Soil type is a crucial factor in investigating potential groundwater mapping as the penetration capability of an area controlled by pore spaces of soil (Tariq, Jiango, Lu, et al. 2023). Similarly, the soil is a significant conditioning parameter in the groundwater poten- tial zone mapping. The soil’s texture and structure determine its permeability, which in turn represents the soil’s capacity for allowing water and other substances to penetrate it (Tariq, Mumtaz, et al. 2023). The soil type map of the research region is produced from FAO and the soil survey of Pakistan, as shown in Figure 3j. 2.3.2.11. NDVI. There is a secondary association between NDVI with groundwater. For example, the region increases plant density, the groundwater table decrease, and vice versa. The value of NDVI in the various depths of the water table revealed that dense vegetation occurs in shallow water regions (Islam et al. 2022). TheNDVI map of theKohat region is calculated from Sentinel-2 using machine learning techniques in GEE. The final NDVI map was reclassified into two classes in the GIS environment, as shown in Figure 3k. 2.3.2.12. Road distance. The road distance of District Kohat is generated using the Google Earth platform and road network map of KPK Highway Authority in ArcGIS platform as shown in Figure 3l. 3. Ground water potential zone mapping models Geospatial modelling was applied in the current research to evaluate the association of groundwater conditioning parameters and groundwater inventory data to generate GWPZ of the Kohat region. The explanation of the applied three models in the pre- sent study is as follows. 3.1. WOE model This GIS-based technique employed linear logic based on Bayesian law to combine data to approximate events’ non-conditional and conditional probability (Elmoulat et al. 2015). WOE models compute the spatial association of dependent variables, i.e. water bodies’ location and independent variables like groundwater potential mapping condi- tioning parameter and compute the weight of each class of parameters. The WOE method was first considered to evaluate mineral potential mapping using GIS-based 10 F. ISLAM ET AL. modelling (Bonham-Carter et al. 1989). In this technique, the W and W weights should be considered as the dynamic aspects. The weight of conditioning factors (B) established on the existence or non-existence of the water bodies (C) of the study region is estimated using the following Eqs. (1)–(3) (Bonham-Carter et al. 1989). þ C W ¼ ln (1) ::: W ¼ ln (2) ::: L ¼ W W (3) In the mentioned equation, p is the likelihood and ln is the natural logs. However, BB and BB are the existence and nonexistence in the causative factor, correspondingly. ::: ::: Similarly, CC and C C show the occurrence and absence of inventory, respectively (Xu et al. 2012). W signifies the occurrence of the conditioning parameters at the spatial posi- tions. Its amount demonstrates the positive relationship between conditioning parameters and water bodies occurrence, respectively. While W represents the nonappearance of groundwater parameters and suggests the level of a contrary relationship. 3.2. FR model The FR technique is the finest bivariate statistical model applied as a valuable GIS- based model for evaluating groundwater inventory and groundwater conditioning parameters (Guru et al. 2017). Currently, the FR model has been effectively utilized for GWPZ in various regions of the world. The FR value equal to or greater than one shows a strong positive correlation between different variables. The following Eq. (4) calculation is applied to compute FR for all causative factors in the present study (Ahmad et al. 2022). E=F FR ¼ (4) M=L Where FR ¼ Frequency Ratio for each conditioning parameter, E ¼ number of water body pixels in each landslide’s causative parameter class, F ¼ total number of all well pixels in research region, M ¼ number of pixels in each landslide condition- ing factors, L ¼ total number of all pixels in study area. 3.3. IV model In the current research, the IV model is applied to make GWPZ of the Kohat region. IV is one of the most suitable practices for choosing significant parameters, ranking variables based on their position, and computing their association with inventory GEOMATICS, NATURAL HAZARDS AND RISK 11 data of the study area in the predictive model (Pardeshi et al. 2013). IV model was first improved by Shano et al. (2020). This article considers the IV for each parameter class based on the presence of groundwater inventory pixels in the given region. The computed information value supports governing the role of each parameter class for groundwater occurrence (Ali et al. 2023). The conditional probability was calculated by dividing the groundwater pixels in each parameter class into pixels of a subclass of groundwater parameter, while the prior probability was considered by dividing the total groundwater pixels in the research region by the entire pixels in the whole research region using the Eq. (5) (Pardeshi et al. 2013). MQoxðÞ R MQ xRðÞ o o W ¼ log (5) MQ xRðÞ o o MQ xRðÞ o o W symbolize the weight of parameters for groundwater. MoxðÞ R illustrates water number of pixels within class ‘o’. MQ xMðÞ number of all pixels within class ‘o’, o o MQ xRðÞ total number of water pixels MQ xMðÞ) practice for entire number of O O O i pixels in region. 3.4. Delineation of the GWPZs 3.4.1. Delineation of groundwater using WOE The groundwater potential index (GWPI) was calculated (Eq. (6)) and mapped based on s values. GWPI ¼ s þ s ::: þ s (6) 1 2 n where s is the final weight for the WOE model. 3.4.2. Delineation of groundwater using FR In contrast to the WOE, the weightage of each class in FR is not determined based on the characteristics of the conditioning factor; instead, it is given in the form of the spatial occurrence of the wells in each class. This contrasts with the WOE, which determines the weightage of each class based on the properties of the conditioning factor. Similarly, the FR is computed for each of the conditioning variables. The succeeding scientific Eq. (7) has been applied to produce GWPZ of the Kohat region (Guru et al. 2017). GWPZM ¼ FR (7) ij i¼1 3.4.3. Delineation of groundwater using IV The GWPZ can be produced for Kohat region using the Eq. (8). GWPZ ¼ W þ W þ W þ W þ W þ W þ W þ W þ W þ W (8) E S A C LULC L P F R D 12 F. ISLAM ET AL. W ¼ Weight of Elevation, W ¼ Weight of Slope, W ¼ Weight of Aspect, W E S A C Weight of Curvature, W ¼ Weight of Landuse Landcover, W ¼ Weight of LULC L Lithology, W ¼ Weight of fault, W ¼ Weight of Road, W ¼ Weight of Rainfall, F R P W ¼ Weight of stream network. 3.5. Validation of the GWPM Evaluation of generated GWPZ is crucial because models without validation have no empirical value. Rather than using the hydraulic parameter of specific capacity, as previous studies did, an indirect indicator of groundwater yield measurement was used in the present research (Jha et al. 2010). From a groundwater sustainability point of view, groundwater yield measurement has been used widely by several researchers such as (Qureshi et al. 2010; Pardeshi et al. 2013; Ji et al. 2015; Fayez et al. 2018; Arabameri et al. 2019) for validation of GWPZ. For many investigations, the receiver operating characteristics (ROC) curve has been the gold standard for evaluating the precision of the GWPZ (Shirazi et al. 2012). The area under the AUC measures the accuracy with which a prediction system can determine whether or not an incident will occur (Shah et al. 2022). In order to validate the WOE, RF and IV-generated GWPZ, the healthy dataset (20%) was used for testing. Areas under the ROC curve were used to evaluate the GWPZ, spatial efficacy (AUC). The rate explains the accur- acy with which the model and influencing variables predict the potential. AUC deter- mines which model is superior, and the one with the most outstanding value wins (Rahman 2008). 4. Results In this article, we developed an inventory of groundwater bodies from Sentinel-2, imageries using various advanced JavaScript algorithms, Google Earth Pro and Google Earth images. The spatial location of surface water bodies like well, ponds, and springs is mentioned in Figure 1. In the present research, we accomplished three bivariate models to generate GWPZ for the Kohat area. 4.1. WOE model The contrast value can be computed from the calculation of both mentioned weights and calculate the association of both dependent and independent variables. The con- cluding LSM of the Kohat region is mentioned in Figure 4. Table 2 shows the analyt- ical results of GIS-based models. The two variables’ low correlation shows groundwater’s low potential zone, and the high value illustrates the high groundwater potential zone in the research region. Based on the results of the WOE model in the elevation parameter, an altitude less than 500 m shows a strong association with groundwater. However, more than 800 m elevation class shows the slightest relation- ship with groundwater. GEOMATICS, NATURAL HAZARDS AND RISK 13 Figure 4. The WOE model for groundwater potential zone. 4.2. FR model The ultimate output map by FR is mentioned in Figure 5. Estimating the GWPZ with the FR model cannot be overstated. FR model carried out the GWPZ by correlating the various variables that conditioned the water with the specific locations of bore wells. In addition, a more excellent correlation value suggests a more significant groundwater potential and vice versa. Finally, LULC classes have a significant bearing on the effect of industrialization on the potential of groundwater. According to the findings of this research, the water body was a factor in the highly prospective ability of 9.923. The mining/industrial region and the vegetation cover area both discovered insignificant FR values when the FR was analyzed by the conditioning factor and the bore wells data. This is the case because FR is analyzed. The result suggested a low FR value because more data is needed from these classes’ bore wells. In contrast, the vegetation cover was always found to influence the infiltration rate significantly. The FR model’s elevation class of less than 500 m, as shown in Table 2 in the pre- sent article, illustrates a strong correlation with the groundwater. In contrast, the less correlated elevation class with groundwater is more than 800 m elevation. In the results of the IV model, as shown in Table 2, the most significant elevation for groundwater is <500 m altitude, while the less critical class is more than 800 m. The results revealed that the correlation value of the WOE, FR, and IV model for eleva- tion classes less than 500 m are 1.62, 3.39, and 1.22, respectively. However, the varia- bles association results show that the correlation value of altitude class > 800 m for WOE, FR, and IV model are 1.84, 0.18, and 1.72, respectively. The results of three 14 F. ISLAM ET AL. Table 2. Statistical analysis for GWPZ of District Kohat, Pakistan. No of Landslide %LS No of pixels pixels % Pixels pixels IV ¼ log Parameters Class in class in a class W W WoE in Class in Class (FR) (A/B) Elevation < 500 8360 149 0.58 0.25 0.83 22.16 39.21 1.77 0.57 500–600 16236 138 0.17 0.11 0.28 43.04 36.32 0.84 0.17 600–700 8448 63 1.49 5.51 7.00 22.40 16.58 0.74 0.55 700–800 3234 22 0.40 0.03 0.43 8.57 5.79 0.68 0.39 > 800 1443 8 0.60 0.02 0.62 3.83 20 3.41 0.60 Slope < 5 2211 76 1.11 0.00 0.73 5.87 20 3.41 1.09 10–20 1186 130 2.35 0.00 2.68 3.15 34.21 10.87 2.25 15–25 5998 70 0.61 0.00 2.36 15.92 18.42 1.16 0.60 25–35 11056 61 0.13 0.00 1.22 29.35 16.05 0.55 0.13 > 35 17223 43 1.54 0.00 1.53 45.72 11.32 0.25 1.53 Aspect F 2779 115 1.44 0.29 1.73 1.44 7.37 30.26 4.11 NE 2970 31 0.03 0.00 0.04 0.03 7.87 8.16 1.04 E 5471 42 0.28 0.04 0.32 0.28 14.50 11.05 0.76 SE 5673 49 0.16 0.03 0.18 0.16 15.04 12.89 0.86 S 5416 27 0.71 0.08 0.79 0.71 14.36 7.11 0.49 SW 3920 41 0.04 0.00 0.04 0.04 10.39 10.79 1.04 W 4073 21 0.68 0.06 0.73 0.68 10.80 5.53 0.51 NW 3762 27 0.34 0.03 0.38 0.34 9.97 7.11 0.71 N 3610 27 0.30 0.03 0.33 0.30 9.57 7.11 0.74 Curvature Concave 14057 213 0.4 0.4 0.77 37.27 56.05 1.50 0.41 Flat 6955 117 0.5 0.2 0.69 18.44 30.79 1.67 0.51 Convex 16709 50 1.2 0.4 1.67 44.30 13.16 0.30 1.21 Distance < 200 5359 206 1.36 0.63 2.00 14.27 54.21 3.80 1.33 to Stream 200–400 6009 66 0.08 0.02 0.10 16.01 17.37 1.09 0.08 400–600 6951 45 0.45 0.08 0.53 18.51 11.84 0.64 0.45 600–800 9656 33 1.09 0.21 1.30 25.72 8.68 0.34 1.09 > 800 9069 30 1.13 0.20 1.32 24.16 7.89 0.33 1.12 Precipitation <900 5829 30 0.68 0.09 0.77 15.49 7.89 0.51 0.67 (mm/year) 900–950 10341 62 0.53 0.14 0.67 27.47 16.32 0.59 0.52 950–1000 11035 113 0.01 0.01 0.02 29.32 29.74 1.01 0.01 1000–1050 5823 115 0.68 0.19 0.87 15.47 30.26 1.96 0.67 >1050 4612 60 0.26 0.04 0.30 12.25 15.79 1.29 0.25 LULC Water 200 150 5.69 0.50 6.19 0.53 39.47 74.41 4.31 Trees 337 10 1.10 0.02 1.12 0.89 2.63 2.94 1.08 Crops 3581 52 0.37 0.05 0.42 9.50 13.68 1.44 0.37 Builtup Area 1650 6 1.03 0.03 1.06 4.38 1.58 0.36 1.02 Bare Ground 234 2 0.17 0.00 0.17 0.62 0.53 0.85 0.16 Scrub/Shrub 31700 160 0.70 1.32 2.01 84.08 42.11 0.50 0.69 Lithology Mss 4802 36 0.30 0.04 0.34 12.73 9.47 0.74 0.30 Q 7565 131 0.55 0.20 0.75 20.06 34.47 1.72 0.54 R 16554 175 0.05 0.04 0.09 43.89 46.05 1.05 0.05 Pal 8797 38 0.85 0.16 1.01 23.32 10 0.43 0.85 Fault Buffer <500 1300 40 1.14 0.08 1.21 3.45 10.53 3.05 1.12 1500 2381 62 0.97 0.11 1.08 6.31 16.32 2.58 0.95 3000 3420 40 0.15 0.02 0.17 9.07 10.53 1.16 0.15 5000 4341 45 0.03 0.00 0.03 11.51 11.84 1.03 0.03 >5000 26285 193 0.32 0.49 0.81 69.68 50.79 0.73 0.32 Soil Loamy and 7510 54 0.67 0.64 1.30 53 21 0.71 0.66 shallow soil Mainly 30035 326 0.64 0.67 1.55 27 57 2.69 1.23 loamy soil NDVI Low 15406 117 0.29 0.16 0.45 40.91 40.91 0.01 0.28 High 22252 263 0.16 0.29 0.45 59.09 59.09 0.01 0.16 Distance < 1000 3145 22 0.37 0.03 0.40 8.34 5.79 0.69 0.36 to Road 1000–2000 3993 40 0.01 0.00 0.01 10.58 10.53 0.99 0.01 2000–3000 4328 39 0.11 0.01 0.13 11.47 10.26 0.89 0.11 3000–4000 3569 70 0.68 0.11 0.78 9.46 18.42 1.95 0.67 > 4000 22693 209 0.09 0.12 0.21 60.15 55 0.91 0.09 GEOMATICS, NATURAL HAZARDS AND RISK 15 Figure 5. The FR model for groundwater potential. bivariate models for elevation illustrate that low elevated area is more permeable and suitable for groundwater potential as compared to high elevated zones of Kohat dis- trict. The slope gradient parameter is considered a very significant parameter for GWPZ. The bivariate results achieved from the association of groundwater conditioning parameters and groundwater pixels in the research region, as mentioned in Table 2, revealed that the most critical class for the groundwater potential is 5 followed by 10 –20 . The results also show that the most significant slope class for the current research area is >35 slope followed by 25 –35 . Based on the slope angle parameter, the slope class with less than 5 has the maximum weight. The association rank for both variables in the present work for 5 by WOE, FR, and IV are 2.68, 3,5, and 2.37, respectively. The correlation between water inventory and groundwater condi- tioning parameters for slopes over 35 are 1.45, 0.25, and 1.53 for WOE, FR, and IV models, respectively. 4.3. IV model The analytical values, as shown in Table 2 for the current research analysis between dependent and independent variables. It illustrated that a lower slope has the greatest like- lihood of groundwater potential. In contrast, a steep slope has adverse impacts on the occurrence of groundwater due to high runoff in high slopes region. The analytical results of bivariate models explained that F is the most vital class of aspect, followed by NE of the Kohat area. The correlation results of the WOE, FR, and IV model for the F direction are 16 F. ISLAM ET AL. Figure 6. The IV model for groundwater potential. 1.73, 4.11, and 1.47, respectively. The NE and SW classes of aspects follow the F class of aspects. The less significant class of aspect is the S direction having 0.71, 0.65, and 0.70 for WOE, FR, and IV model. According to the analysis for the association between groundwater data and curvature shape, the concave structure has the highest correlation value, i.e. 0.99, 1.65, and 0.50 for WOE, FR, and IV, respectively. The concave structure is the most significant class of curvature for groundwater potential zone mapping. The results revealed that the spatial association of groundwater and conditioning parameters for WOE, FR, and IV models are 1.67, 0.29, and 1.21, respectively. As shown in Table 2, the results revealed that the most significant class of curvature is a concave struc- ture, followed by Flat and convex structures. As shown in Table 2, the results revealed that groundwater is more likely to occur in a sense stream. There is a maximum likeli- hood of distance from the river of less than 200 m. The correlation value of less than 200 m class of stream is 2.0, 3.80, and 1.33 for WOE, FR, and IV model, respectively. These results illustrate that fewer distances to rivers have had a more significant influence on groundwater potential (Figure 6). However, most of the less significant class of stream parameters are greater than 800 m, followed by a 600 m–800m range. The association between WOE, FR, and IV variables are 1.32, 0.33, and 1.12, respectively, for more significant than 800 m class of stream. In the current research, the precipitation map was formed from CHIRPS sat- ellite data, followed by the reclassification into five categories to assess the relationship of rainwater factor with groundwater bodies. The outcomes, as revealed in Table 2 for rainfall, supported that rainfall is a significant aspect of groundwater potential. The results show that the 1000–1050 mm/year precipitation class is the most significant for groundwater potential, followed by >1050mm/year. The 1000–1050 class correlation GEOMATICS, NATURAL HAZARDS AND RISK 17 between rainfall and groundwater is 1.32, 2.73, and 1.0 for WOE, FR, and IV models. The precipitation class < 900 mm/year has no significant impact on groundwater poten- tial. The bivariate analysis for WOE, FR, and IV are 0.90, 0.45, and 0.79, respect- ively. Considering the above-mentioned statistical facts, it can be concluded that high precipitation classes show more groundwater occurrence and vice versa. In the current research results, the cropland area is the most important and influential class of LULC parameter in the study area. The analytical results of groundwater and conditioning parameters for WOE, FR, and IV are 0.63, 1.70, and 0.53, respectively. The agricultural land shows high potential results for groundwater because the agriculture region is recharged from the irrigation system of the study area. The scrub/shrub, forest, and urban class of LULC follow the agricultural land. The analysis of both variables treasures that Q is the most influential geological forma- tion of lithology parameters for groundwater potential in the Kohat region. The correl- ation of both variables is clear in WOE, FR, and IV model. The results for groundwater potential zone mapping of the present study show that the lithological parameter is the least significant class for groundwater. A fault is a significant parameter for groundwater percolation. Geological faults strongly influence groundwater mobility because they enhance the strata’s mobility mechanism for groundwater. The maximum likelihood of groundwater potential in distances to a fault is <500 m buffer region. The correlation value of both variables of class <500 m for WOE, FR, and IV are 0.60, 1.73, and 0.55, respectively. The geological fault’s > 5000m fault buffer has no significant impact on groundwater. The results of >5000 m buffer for the WOE, FR, and IV model are 0.45, 0.82, and 0.20, respectively. The results concluded that fault is the influential parameter for groundwater potential for the present research area. As shown in Table 2, the results explained that mainly loamy soil is the suitable class for groundwater in the Kohat area. The correlation of both variables for WOE, FR, and IV are 1.55, 2.69, and 1.23, respect- ively. As shown in Table 2, the present research results illustrate that NDVI is a crucial parameter for groundwater potential. Both variables’ association ranks are 0.57, 1.83, and 0.27 for WOE, FR, and IV model, respectively. The results of the present study between NDVI and GWPZ revealed that NDVI and groundwater Table depth are inversely related, i.e. high NDVI will have low water table depth and vice versa for the current investigation. The current study considered the road to compute the association between the road net- work in the Kohat region and the groundwater potential. The results explained that the most influential class of road network is a 3000–4000 m buffer followed by >4000 m and 2000–3000 m buffer. The correlation value for WOE, FR, and IV model are 0.78, 1.95, and 0.67, respectively. Table 2 revealed that the <1000 m class correlation values between the road and groundwater are 0.40, 0.69, and 0.36 for WOE, FR, and IV model, respectively. The results of all road network buffers revealed that the road network has adverse impacts on groundwater in the study area. 4.4. Validation 4.4.1. Validation of models In the modelling technique, validation of the model is a significant phase to accom- plish the reliable scientific worth of the research project (Barakat et al. 2023). In 18 F. ISLAM ET AL. Figure 7. ROC curves for WOE, FR, and IV methods. numerous research, the AUC technique was used to evaluate GWPZ. This ROC curve is considered a standard index for accuracy assessment. This technique has been extensively utilized for assessing techniques applied in various water research investi- gations. The receiver operating characteristics (ROC) graph validated the WOE, FR, and IV models. The region indicates the precision of the prediction or classification under the receiver operating characteristic curve (AUC) (Pourghasemi and Rossi 2017). In this investigation, we have tested and verified three different models derived from the GWPZ’s final categorization. The AUC values range from 0 to 1. If the number is less than 0.5, the model’s classification was inappropriate, and it should be redone. On the other hand, if the value is close to 1, it suggests that the result is clearly defined (Pourghasemi and Rossi 2017). To verify WOE, FR, and IV models, the ROC curves of the GWPZ maps were constructed (Figure 7). The finding demonstrates that the outcome predicted by the FR model for GWPZ (AUC ¼ 91%) is successfully achieved when compared to both the WOE model (AUC ¼ 88%) and the IV model (AUC ¼ 89%). Nevertheless, all the obtained findings were checked for validity and clearly defined (Pourghasemi and Rossi 2017). However, the FR is a better representative for this study area to indicate the spatial distribution of the GWPZ compared to the WOE and FR models. This is because the GWPZ is more likely to be found in areas where the FR model is more accurate. As a result, this validation method is highly recommended for research into potential groundwater evaluation. The generated validation graphs of the applied models in the present study, as mentioned in Figure 7, utilized twenty percent of the inventory data of water. The highest value of the AUC value showed the most reliable results of the model and while the lowest value showed unreliable results. The find- ings by mentioned validation technique for WOE, FR, and IV clearly explained that all applied models are consistent and trustworthy methods to produce GWPZ for the GEOMATICS, NATURAL HAZARDS AND RISK 19 Table 3. Accuracy assessment of land use and land cover. S. No Classes UA PA OA K 1 Water 90.00 91.84 87.00 0.81 2 Trees 88.00 84.62 3 Crops 86.00 87.76 4 Built up area 92.00 86.79 5 Bare ground 86.00 91.49 6 Shrubs 88.00 88.00 Notes: UA ¼ User’s Accuracy, PA ¼ Producer’s Accuracy, OA ¼ Overall Accuracy, K ¼ Kappa Coefficient. Kohat District of Pakistan. The validation outcome specifies that the FR technique is the most reliable method for GWPZ in the study area. 4.4.2. LULC accuracy assessment Accuracy evaluations were performed after obtaining land use/land cover categoriza- tion outcomes. In order to do so, we used the user accuracy matrix, the producer accuracy matrix, and the total accuracy matrix to measure precision. They calculated the users’ accuracy by taking the ratio of adequately classified cells to the total num- ber of reference points. Google Earth was used as a reference tool for this research. The overall accuracy was calculated by dividing properly classified cells by all pixels. In contrast, producer accuracy was calculated by dividing the number of cells with correct land use/land cover classification by the number of ground truth pixels as explained in Table 3. 5. Discussion Due to the increased demand for water availability for urbanization, industrialization, and irrigation purposes, there has been an increase in the research investigation on the groundwater scenario. This is especially true in arid to semi-arid regions world- wide, where the need for groundwater is even more critical. There have been numer- ous research to understand the science behind water recharge and prepare GWPZ for the scientific exploration and management of groundwater (Arabameri et al. 2019). Therefore, proper groundwork and methods should be implemented for GWPZ to manage the groundwater because the execution method for GWPM is still an argued subject (Nampak et al. 2014; Park et al. 2014). This article has emphasized the appreciation of the groundwater potential of the Kohat region of Pakistan has been evaluated using WOE, FR, and IV models. These models were applied to compute the correlation between water body pixels and con- ditioning parameters for groundwater. The lower correlation value represents low potential zones, while the high correlation shows high potential groundwater regions. The probability of groundwater potential generally diminutions with increasing eleva- tion (S. Hasan AL-Zuhairy et al. 2017). In the present study, the spatial analysis dis- closed that the elevation class of < 500 m has a higher correlation value between both variables; however, the > 800 m elevation class revealed no significant associ- ation for groundwater potential. The slope gradient is an influential parameter for groundwater potential because steep slopes are the significant parameter in GWPZ. Moreover, the slope gradient is another significant parameter for groundwater 20 F. ISLAM ET AL. potential. A steep slope gradient adversely impacts on groundwater because it increases the surface run-off and affects the intrusion of precipitation into the ground (Jaiswal et al. 2003). If the slope angle is greater than 35 , groundwater potential is reduced because it restricts the aquifer’s recharge (Madrucci et al. 2008). The current study considers slope angle a critical factor for GWPZ. The association of dependent and independent variables for slope up to 20 is very suitable for the high potential zone of groundwater. In contrast, the slope angle > 35 has an inverse relationship with the groundwater pixel and revealed low groundwater potential, as shown in Table 2 of the results. The flat surface of the aspect is more appropriate for ground- water amount (Manap et al. 2013). In the present study, we observed that the flat surface of the aspect has a strong association with groundwater inventory data. The flat surface correlates with 0.86, 1.97, and 0.68 using WOE, FR, and IV models. The FR and EBF model revealed that concave and convex structures are less associated with groundwater potential than flat regions. Water reservoir and aquifer recharge mainly occurred in the flat region; however, the convex and concave structures did not support the water storage and infiltration (Arabameri et al. 2020). In this study, our results concluded that the Flat class of curvature strongly correlates with groundwater, followed by Concave. At the same time, convex adversely impacts groundwater potential, as shown in Table 2. The most developed likelihood of groundwater is perceived in denser drainage networks. In the current investigation, the < 200 m class of drainage network shows the most influential association with groundwater potential using WOE, FR, and IV technique, followed by 200–400 m and 400–600 m. The relationship ranks of the> 800 m class of drainage revealed that this class has no impact on groundwater potential. The rainfall strongly correlates positively with aquifer recharge (Wu et al. 2020). In this study, the precipitation class 1000–1050mm/year strongly correlates with groundwater potential having a positive correlation y followed by >1050 mm/year. The low precipitated area has no inverse relationship with the groundwater potential of the study area. The pre- cipitation class <900mm/year has minor importance for groundwater potential in the current study area and is followed by 900–950 mm/year Crops and a garden class of the LULC parameter are significantly associated with groundwater having correlation values of 2.06 and 1.25, respectively, demonstrating these classes’ high potential water zones (Falah et al. 2017). In the context of binary classification, the Receiver Operating Characteristic (ROC) curve is a popular method to evaluate and compare the performance of different models. The ROC curve plots the True Positive Rate (TPR) against the False Positive Rate (FPR) for different thresholds of a model’s predicted probability (Li et al. 2021). A model with a higher AUC (Area Under the ROC Curve) is considered better. In this study we used WOE, FR and IV and compare their performance using the ROC curve. Model WOE has an AUC of 88%. This means that it has a good balance between TPR and FPR, with relatively few false positives and false negatives. Model WOE is likely to be a good choice for classification tasks where both precision and recall are important (Wahla et al. 2022). Model FR has an AUC of 0.91. This means that it has a high TPR and low FPR, making it suitable for applications where identifying true positives is crucial, and false GEOMATICS, NATURAL HAZARDS AND RISK 21 positives are less of a concern. However, Model IV may be too aggressive in classify- ing examples as positive, leading to a high false-negative rate. Model IV has an AUC of 91%. This means that it has a higher FPR and lower TPR compared to Model A, but still performs better than random guessing. Model IV may be useful in cases where minimizing false positives is critical, but it may not perform as well in cases where false negatives are costly. In summary, each model has its strengths and weaknesses, and the choice of the appropriate model depends on the specific requirements of the task at hand. Model FR strikes a good balance between TPR and FPR, Model IV is useful when minimiz- ing false positives is crucial, and Model WOE is suitable for identifying true positives at the expense of false negatives. Our research results in the Kohat region of Pakistan showed that cropland is the most influential factor for groundwater potential. The correlation value for cropland in the current research are 0.63, 1.70, and 0.53 for WO, FR, and IV, respectively. Concerning the geological fault buffer, it was hypothesized that the association between both variables for groundwater would weaken the further away from the fault one got. Their relationship increases when the distance from the fault decreases (Falah et al. 2017). Our present study results in the Kohat area presented that fault favours water infiltration and supports the aquifer recharge in the current area. The most effective fault buffer is <500 m because this class shows a strong positive correl- ation of 0.60, 1.73, and 0.55 applying the WOE, FR, and IV model, followed by 1500 m and 3000 m buffers. However, the buffer of >5000 class has no significant role in groundwater potential and recharge of water. The NDVI is a vital parameter for groundwater potential. NDVI and water Table have an inverse relationship, i.e. when the NDVI increases, the water table rise and vice versa (Seeyan et al. 2014). The same scenario we observed in our current research region. The high NDVI zone strongly correlates with groundwater, while the low NDVI region adversely impacts the present area. As shown in Table 2, the results justified the above statement for NDVI association with groundwater. According to the analytical results in Table 2, drainage network,slope,elevation,and rain- fall are the most significant parameters for GWPZ in the present research area. According to GIS-based statistical models, the FR is the best technique for GWPZ in the current research project. Final GWPZ was also produced using GIS-based models and then was classified into five classes of very low, low, moderate, high, and very high groundwater potential zones. The final GWPZ can be helpful for various research organizations like agriculture and energy-related sectors to manage the groundwater in the present study area. 6. Conclusions This article describes a study that aims to investigate potential groundwater zones in the Kohat District of Pakistan using three different GIS-based models: Weight of Evidence (WOE), Frequency Ratio (FR), and Information Value (IV). The study uses various data sources, including satellite imagery, ground surveys, and public health department data, to develop an inventory map of groundwater and twelve ground- water conditioning parameters. The study then applies the three GIS-based models to 22 F. ISLAM ET AL. generate GWPZ maps and categorizes them into five categories based on their poten- tial for groundwater availability. The study finds that stream, slope angle, elevation, and rainfall are the most significant parameters for GWPZ. The study uses ROC curves to assess the accuracy of the models and finds that FR is the most reliable model for the study. The study concludes that the GWPZ maps generated by the WOE, FR, and IV techniques can be useful for research and development agencies to improve groundwater exploration and development planning in the future. Acknowledgements The authors would like to thank the university authority for financial support. The authors thanks to (TURSP-2020/82), Taif University, Taif, Saudi Arabia. Author contributions Fakhrul Islam: methodology, software, formal analysis, visualization, data curation, writing— original draft, investigation, validation, writing—review and editing, Aqil Tariq: formal ana- lysis, visualization, data curation, writing—review and editing, Supervision. Rufat Guluzade: writing—review and editing. Na Zhao: Funding, writing review and editing, Safeer Ullah Shah: data curation, writing—original draft, investigation, validation, writing—review and edit- ing, Matee Ullah: writing—review and editing. Mian Luqman Hussain: writing—review and editing. Muhammad Nasar Ahmad: writing—review and editing, Abdulrahman Alasmari: writing—review and editing, Fahad M. Alzuaibr: writing—review and editing, Ahmad El Askary: writing—review and editing, Muhammad Aslam: writing—review and editing. All authors have read and agreed to the published version of the manuscript. Funding The Key Project of Innovation LREIS (KPI001). The authors would like to thank the university authority for financial support. The authors thanks to (TURSP-2020/82), Taif University, Taif, Saudi Arabia. ORCID Aqil Tariq http://orcid.org/0000-0003-1196-1248 Abdulrahman Alasmari http://orcid.org/0000-0003-1212-8581 Data availability statement st Data available on the reasonable request from the 1 author of this article. Disclosure statement No potential conflict of interest was reported by the authors. GEOMATICS, NATURAL HAZARDS AND RISK 23 References Ahmad I, Dar MA, Teka AH, Teshome M, Andualem TG, Teshome A, Shafi T. 2020. GIS and fuzzy logic techniques-based demarcation of groundwater potential zones: a case study from Jemma River basin, Ethiopia. J African Earth Sci. 169:103860. Ahmad MN, Shao Z, Aslam RW, Ahmad I, Liao M, Li X, Song Y. 2022. 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http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.pngGeomatics Natural Hazards and RiskTaylor & Francishttp://www.deepdyve.com/lp/taylor-francis/comparative-analysis-of-gis-and-rs-based-models-for-delineation-of-S1JES0Lcve
Comparative analysis of GIS and RS based models for delineation of groundwater potential zone mapping
Comparative analysis of GIS and RS based models for delineation of groundwater potential zone mapping
Abstract
Abstract Groundwater is a crucial natural resource that varies in quality and quantity across Khyber Pakhtunkhwa (KPK), Pakistan. Increased population and urbanization place enormous demands on groundwater supplies, reducing both their quality and quantity. This research aimed to delineate the groundwater potential zone in the Kohat region, Pakistan by integrating twelve thematic layers. In the current research, Groundwater Potential Zone (GWPZ) were created by implementing Weight of...
GEOMATICS, NATURAL HAZARDS AND RISK 2023, VOL. 14, NO. 1, 2216852 https://doi.org/10.1080/19475705.2023.2216852 Comparative analysis of GIS and RS based models for delineation of groundwater potential zone mapping a b,c d e f Fakhrul Islam , Aqil Tariq , Rufat Guluzade , Na Zhao , Safeer Ullah Shah , g h c Matee Ullah , Mian Luqman Hussain , Muhammad Nasar Ahmad , Abdulrahman i i j k Alasmari , Fahad M. Alzuaibr , Ahmad El Askary and Muhammad Aslam a b Department of Geology, Khushal Khan Khattak University, Karak, Pakistan; Department of Wildlife, Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, MS, USA; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China; School of Earth Science and Engineering, majoring in Geodesy and Survey Engineering, Hohai University, Nanjing, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Board of Revenue, Government of Pakistan, Peshawar, KPK, Pakistan; Faculty of Earth sciences, Geography and Astronomy, University of Vienna, Vienna, Austria; National Centre of Excellence in Geology, University of Peshawar, Peshawar, KPK, Pakistan; Department of Biology, Faculty of Science, University of Tabuk, Tabuk, Saudi Arabia; Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia; School of Computing Engineering and Physical Sciences, University of West of Scotland, Paisley, UK ABSTRACT ARTICLE HISTORY Received 15 February 2023 Groundwater is a crucial natural resource that varies in quality and Accepted 17 May 2023 quantity across Khyber Pakhtunkhwa (KPK), Pakistan. Increased popu- lation and urbanization place enormous demands on groundwater KEYWORDS supplies, reducing both their quality and quantity. This research aimed Ground water potential to delineate the groundwater potential zone in the Kohat region, zones; GIS; RS; FR; AUC Pakistan by integrating twelve thematic layers. In the current research, Groundwater Potential Zone (GWPZ) were created by implementing Weight of Evidence (WOE), Frequency Ratio (FR), and Information Value (IV) models of the Kohat region. In this study, we used Sentinel- 2 satellite data were utilized to generate an inventory map of ground- water using machine learning algorithms in Google Earth Engine (GEE). Furthermore, the validation was done with a field survey and ground data. The inventory data was divided into training (80%) and testing (20%) datasets. The WOE, FR, and IV models are applied to assess the relationship between inventory data and groundwater fac- tors to generate the GWPZ of the Kohat region. Finally, the current research results of Area Under Curve (AUC) technique for WOE, FR, and IV models were 88%, 91%, and 89%. The final GWPZ can aid in better future planning for groundwater exploration, management, and supply of water in the Kohat region. CONTACT Na Zhao zhaon@lries.ac.cn 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. 2 F. ISLAM ET AL. 1. Introduction Groundwater is an important natural resource that makes up about 34% of the world’s freshwater supply (Tariq, Siddiqui, et al. 2022). It is the primary water supply and is regarded as less contaminated than other water sources. It supplies approxi- mately half of the freshwater that can easily be accessed and used for cleaning, drink- ing, and cooking regularly (Termeh et al. 2019). Groundwater meets the requirements of 97% of the world’s population for freshwater and provides 50% of the world’s irri- gation (Tariq and Shu 2020). It can be considered the essential capital natural posses- sions that occurred in the sediments and fractures of soil and rock (Ahmad et al. 2020). Groundwater is commonly utilized for domestic, industrial, and farming pur- pose in numerous parts of the world (Mumtaz et al. 2023). The tremendous demand for groundwater is increasing rapidly, and this rising need for water frequently causes overutilization, which is striking massive stress on the inadequate groundwater source. Furthermore, freshwater matter has become a critical issue in the tropical and subtropical areas of the world due to unscientific irrigation, exploration, urbanization, and changes in climatic factors. Furthermore, these methods only sometimes account for the several parameters that influence groundwater’s existence, storage, and mobility in rocks and soil (Keesstra et al. 2012). While currently, with the advancement of computer technology, geospatial techniques have become the most effective, emerging, and innovative ways to delineate potential groundwater regions. These methods can be applied locally and regionally based using ground and satellite data. These ground and remote sensing (RS) data are processed in the geographic information system (GIS) platform to detect and categorize the influencing parameters of groundwater (Basharat et al. 2022). RS provides inexpensive data as input to the GIS platform for the regional and inaccessible regions with a temporal, spectral, and spatial resolution (Majeed et al. 2022; Sadiq Fareed et al. 2022; Tariq, Mumtaz, et al. 2023). Satellite data can compute geological information (fault, fold, fractures, lithology) and topographic, climatic, and hydrological parameters for groundwater assessment. Geospatial technology is an innovative science to collect, store, display, and analyze various ground and RS data for groundwater in the form of surface water inventory (e.g. dams, ponds, open wells, and springs), groundwater demarcation, surface water modelling, and groundwater contamination (Siddiqui et al. 2020; Zainab et al. 2021; Tariq, Yan, et al. 2022; Tariq, Jiango, Li, et al. 2023). In the past, numerous GIS and RS based models have been applied by scientist, i.e. Evidential Belief Function (EBF) (Shah et al. 2021), Weight of Evidence (WOE) (Lee, Kim, et al. 2012), Frequency Ratio (FR) (S. Hasan AL-Zuhairy et al. 2017), Decision Tree (DT), (Lee, Song, et al. 2012), Classification and Regression Tree (CART), (Mohammadi et al. 2021), Boosted Regression Tree (BRT), (Kordestani et al. 2019), Artificial Neural Network (ANN), (Lee, Song, et al. 2012), Multivariate Adaptive Regression Splines (MARS), (Kalantar et al. 2018), Binary Logistic Regression (BLR), (Chen et al. 2018), Analytic Hierarchy Process (AHP), (Singh et al. 2018), Random Forest (RF), (Tariq et al. 2021), Fuzzy Logic (FL), (Shahid et al. 2002), Support Vector Machine (SVM), (Lee et al. 2017), Multi-criteria Decision Analysis (MCDA) GEOMATICS, NATURAL HAZARDS AND RISK 3 (Kaliraj et al. 2014), Quadratic Discriminant Analysis (QDA) (Baloch et al. 2021), K- Nearest Neighbour (KNN) (Naghibi et al. 2018). The SVM is the technique to predict Groundwater Potential Zone (GWPZ) (Eid et al. 2023). Groundwater is most vital and significant natural resource for sustainability due to its agriculture-dependent economy in Pakistan. Groundwater is a vital element in the economy, but human population, industrialization, unscientific exploration, and groundwater mismanagement have twisted a chief risk to this treasured energy source (Moazzam et al. 2022). Therefore, GWPZ is an indispensable technique for mapping and managing the precious water resources in the area of interest (Baloch et al. 2021). Numerous field survey mechanisms, i.e. geological, geophysical, and hydro- logical studies, have been used by researchers to demarcate potential groundwater zones (Israil et al. 2006). These methods need several human resources financial budget, and it can be most time-consuming. In this research, WOE, FR, and IV models were utilized to locate the GWPZ in the KPK region of Pakistan. Although several studies have been conducted across Pakistan utilizing RS and GIS techniques to delineate the groundwater potential map, none of those studies have been conducted in this region where sustainable ground- water resources management is essential for the industrial, commercial development, and economy of the country. In the current study, we used twelve influencing param- eters that are considered significant to explore the deficient, low, medium, high, and very high potential groundwater regions. These parameters were prepared in the GIS platform from various ground and RS data. In the current research, three geospatial techniques were used to compute the association of influencing parameters with groundwater inventory data and to delineate potential groundwater regions in district Kohat, Pakistan. These GIS-based groundwater mapping models have not been inves- tigated previously in the district Kohat. The final GWPZ map can be helpful for deci- sion-makers to assess and manage groundwater in various regions of the study area. 2. Material and methodology 2.1. Study area The current research is conducted in district Kohat, situated in the southern part of Khyber Pakhtunkhwa (KPK), Pakistan. The Kohat region is geographically extended 0 00 0 00 0 00 0 00 from 33 35 13 33 49 73 N and 71 52 49 Eto 71 26 32 (Figure 1a–c) (Hussain 2014). The study area occurred at an elevation of about 2000 m. Climatically the research region is considered a limited steppe climate region with slight precipitation throughout the year. In Kohat District, the summers are long, hot, humid, and clear, while the winters are brief, cold, and mostly clear. Both seasons have clear skies. Temperatures below 0.5 C or above 43.33 C are extremely uncommon throughout the year. On average, the temperature ranges from 2.2 Cto 39.45 C (Azra et al. 2019). Geologically the current study area is situated in Kohat Plateau. The study area includes fold and thrust belt collections which are thin- skinned structures covered by thick-skinned structures. Compressional structures sub- ject a significant portion of the plateau; however, the strike-slip faulting is limited to the southern Kohat plateau (Hussain and Zhang 2018). The Kohat plateau is mainly 4 F. ISLAM ET AL. Figure 1. (a) Geographical location of Pakistan, (b) Provincial boundary of KPK where study area exists, and (c) Location map of study area with elevation. occupied by lithologies of Eocene limestone, shale, evaporates, and subordinate clays, and younger clastic sedimentary rocks of the Miocene–Pliocene age (Hussain et al. 2021). The age of sedimentary rocks in the plateau is composed of Paleocene to Pliocene, which was first deposited on the northern Indian plate margin (Tariq and Qin 2023). 2.2. Datasets Various datasets are applied to generate different parameters used in the current work. The datasets utilized in the current research comprise organization ground (field survey and ground data) and RS data. The ground and satellite information applied to prepare twelve influential parameters for groundwater potential were acquired from appropriate national and international research platforms. The data details and sources of information are mentioned in Table 1. 2.3. Methodology The study was established in four phases: i) preparation of ground water inventory map of the study area using different geospatial, machine learning, and field survey GEOMATICS, NATURAL HAZARDS AND RISK 5 Table 1. Description of datasets were used in this research. Data Scale/resolution Data availability Data availability statement/source Maps of parameters Sentinel 2 10 m Freely available The data supporting this study’s findings are openly available in [USGS] at LULC, NDVI Map and ground https://earthexplorer.usgs.gov. water inventory ALOS DEM 12.5m Freely available The data that support the findings of this study are openly Elevation, Slope, Aspect, Curvature, available at https://asf.alaska.edu and drainage CHIRPS 0.05 Freely available The data supporting this study’s findings are available in [UCSB] at Rainfall maps https://www.chc.ucsb.edu. The spatial resolution of CHIPRS is 0:05 (5.54 Km) and daily gridded. Soil 1: 2000000 Freely available le Freely available at Soil type of Pakistan and FAO Soil Texture Map Geology 1:650000 Geological Map Lithology and Fault map 6 F. ISLAM ET AL. Figure 2. Flowchart of present research work for the current research study. techniques, ii) generation of twelve influential groundwater parameters, iii) generating GWPZ using three geospatial models like WOE, FR, and IV and, vi) performing val- idation and accuracy assessment using AUC technique. The comprehensively organ- ized methodology for the present investigation is shown in Figure 2. 2.3.1. Inventory map of surface water bodies The accurate water inventory map is the primary and essential parameter to generate GWPZ for the region of interest. The ground and RS data for the inventory map were collected from various public organizations and satellite sources. The inventory map of different water bodies was prepared from Sentinel-2 using a ML-model. The inventory map was validated and verified with ground data collected from the public department of district Kohat and various field surveys in the Kohat region. Finally, detected inventory data of current research is divided into training (80%) and testing (20%) datasets (Zhu et al. 2022). 2.3.2. Preparation of GWPZ conditioning parameters Considering groundwater potential, conditioning parameters is a significant task affecting the final output map of GWPZ; hence, conditioning parameters should be cautiously designated (Bui et al. 2019). The existence and yield of groundwater in a specified aquifer are influenced by numerous parameters. In the present study, twelve influential conditioning factors like elevation, slope angle, aspect, curvature, drainage network, rainfall, LULC, soil, NDVI and the road distance are considered to evaluate the influences of mentioned parameters on groundwater potential in the study area. GEOMATICS, NATURAL HAZARDS AND RISK 7 Figure 3. Parameters for GWPZ; (a) elevation, (b) slope, (c) aspect, (d) curvature (e) drainage net- work (f) rainfall, (g) LULC, (h) lithology, (i) fault distance, (j) soil types, (k) NDVI (l) road distance. 2.3.2.1. Elevation. Altitude influences the potential groundwater zone as it is con- versely associated with the reservoir (Karimi-Rizvandi et al. 2021). The altitude of the present research area is computed from having 12.5 m spatial resolution and reclassi- fied into five categories in ArcMap 10.8 as revealed in Figure 3a. 2.3.2.2. Slope. The slope gradient is another significant parameter for groundwater potential because the slope angle directly influences the amount of rainwater water intrusion and surface run-off in any region. A steep slope gradient negatively impacts groundwater reservoirs because a higher slope enables a rapid run-off area and reduces water infiltration. In contrast, a low slope promotes water infiltration and potential recharge area (Maskooni et al. 2020). The slope of the Kohat region reclas- sify into five classes, i.e. <5 ,5–15 ,15–25 ,25–35 and >35 using ArcGIS 10.8 as shown in Figure 3b. The highest slope of the present research area is verified as 78 , while the lowest slope of the region is recorded as 0 . 2.3.2.3. Aspect. The slope aspect presents slope directions that affect the quantity of precipitation, radiation of the sun, wind speed, and LULC, which concomitantly strike the amount of water permeation to the pore spaces of sediments influencing groundwater potential in the region (Solomon and Quiel 2006). The aspect of the present area is generated and reclassified into nine classes, as revealed in Figure 3c. 8 F. ISLAM ET AL. 2.3.2.4. Curvature. The curvature map shows the association with the capacity to store and hold water reserves on the area of surface. Usually, the dipped structures accumulate more water bodies (Pham et al. 2019). The curvature of the Kohat region is calculated from ALOS DEM having 12.5 m resolution and reclassified into open, flat, and convex groups, as mentioned in Figure 3d. 2.3.2.5. Drainage network. Drainage network presents an inverse association with the percolation of water in fracture and sediments of strata because river density discour- ages water retention (Kordestani et al. 2019). As the river network density is high, water recharge in the area will be low and vice versa because river density favours surface runoff and decreases infiltration. The five buffers were applied to the stream network of the present research area stream, as shown in Figure 3e. 2.3.2.6. Rainfall. Climatic parameters are influential in controlling the water table from the seasonal perception. Precipitation is a significant climatic factor that affects groundwater recharge. Precipitation is considered a vital parameter for potential groundwater mapping because the probabilities of penetration are more in cases of high rainfall; consequently a chief source of water recharge in the area (da Silva Monteiro et al. 2022). The precipitation of the present study area is computed from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) of 2010 to 2022 using a ML Algorithm in GEE. The concluding precipitation map was then reclassified into five classes using GIS environment as shown in Figure 3f. 2.3.2.7. LULC. LULC is a significant parameter influencing groundwater recharge, occurrence, and availability. LULC presents environmental parameters having a sig- nificant impression on groundwater because they affect penetration and surface run- off (Bui et al. 2019). Moreover, bare ground and built-up regions usually display low potential, while vegetation and the area near water reservoirs illustrate higher ground- water potential. LULC map of the Kohat region is generated from Sentinel-2 data in GEE using a ML algorithm. This causative factor was further categorized into six classes for evaluating these classes on groundwater, as shown in Figure 3g. Confusion matrices were used to create classification accuracy processes, such as overall accuracy, omission and commission errors, and Cohen’s kappa statistic. These classification accuracy matrices were derived using confusion matrices (Firdaus 2014). As per reference data, the commission error is the percentage of pixels that have been incorrectly assigned to classes they do not belong. On the other hand, the per- centage of pixels that should have been assigned to a particular class according to the reference data but have not been assigned to that class is an omission error. It was figured out how to determine the omission and commission error for each LULC class and estimate the average for all classes. 2.3.2.8. Lithology. The lithology of strata controls the porosity and permeability of aquifers and influences groundwater due to its conductivity and penetration. These rock and soil properties affect groundwater’s existence, accumulation, and mobility GEOMATICS, NATURAL HAZARDS AND RISK 9 (Muavhi et al. 2022). The lithology of the Kohat region is extracted from the Northern Geological Map of Pakistan, as shown in Figure 3h. 2.3.2.9. Fault distance. The fault Buffer of various distance gaps was selected for ana- lysis because it influences the subsurface flow of fluids (Yin et al. 2018). Therefore, geological faults and fractures are critical parameters in detecting groundwater sour- ces. The fault parameter is digitized in this research, as shown in Figure 3i. Five buf- fers were applied to calculate the relationship of fault with groundwater potential in the study area. 2.3.2.10. Soil types. Soil is the uppermost horizon of land which helps in water infiltra- tion. Soil type is a crucial factor in investigating potential groundwater mapping as the penetration capability of an area controlled by pore spaces of soil (Tariq, Jiango, Lu, et al. 2023). Similarly, the soil is a significant conditioning parameter in the groundwater poten- tial zone mapping. The soil’s texture and structure determine its permeability, which in turn represents the soil’s capacity for allowing water and other substances to penetrate it (Tariq, Mumtaz, et al. 2023). The soil type map of the research region is produced from FAO and the soil survey of Pakistan, as shown in Figure 3j. 2.3.2.11. NDVI. There is a secondary association between NDVI with groundwater. For example, the region increases plant density, the groundwater table decrease, and vice versa. The value of NDVI in the various depths of the water table revealed that dense vegetation occurs in shallow water regions (Islam et al. 2022). TheNDVI map of theKohat region is calculated from Sentinel-2 using machine learning techniques in GEE. The final NDVI map was reclassified into two classes in the GIS environment, as shown in Figure 3k. 2.3.2.12. Road distance. The road distance of District Kohat is generated using the Google Earth platform and road network map of KPK Highway Authority in ArcGIS platform as shown in Figure 3l. 3. Ground water potential zone mapping models Geospatial modelling was applied in the current research to evaluate the association of groundwater conditioning parameters and groundwater inventory data to generate GWPZ of the Kohat region. The explanation of the applied three models in the pre- sent study is as follows. 3.1. WOE model This GIS-based technique employed linear logic based on Bayesian law to combine data to approximate events’ non-conditional and conditional probability (Elmoulat et al. 2015). WOE models compute the spatial association of dependent variables, i.e. water bodies’ location and independent variables like groundwater potential mapping condi- tioning parameter and compute the weight of each class of parameters. The WOE method was first considered to evaluate mineral potential mapping using GIS-based 10 F. ISLAM ET AL. modelling (Bonham-Carter et al. 1989). In this technique, the W and W weights should be considered as the dynamic aspects. The weight of conditioning factors (B) established on the existence or non-existence of the water bodies (C) of the study region is estimated using the following Eqs. (1)–(3) (Bonham-Carter et al. 1989). þ C W ¼ ln (1) ::: W ¼ ln (2) ::: L ¼ W W (3) In the mentioned equation, p is the likelihood and ln is the natural logs. However, BB and BB are the existence and nonexistence in the causative factor, correspondingly. ::: ::: Similarly, CC and C C show the occurrence and absence of inventory, respectively (Xu et al. 2012). W signifies the occurrence of the conditioning parameters at the spatial posi- tions. Its amount demonstrates the positive relationship between conditioning parameters and water bodies occurrence, respectively. While W represents the nonappearance of groundwater parameters and suggests the level of a contrary relationship. 3.2. FR model The FR technique is the finest bivariate statistical model applied as a valuable GIS- based model for evaluating groundwater inventory and groundwater conditioning parameters (Guru et al. 2017). Currently, the FR model has been effectively utilized for GWPZ in various regions of the world. The FR value equal to or greater than one shows a strong positive correlation between different variables. The following Eq. (4) calculation is applied to compute FR for all causative factors in the present study (Ahmad et al. 2022). E=F FR ¼ (4) M=L Where FR ¼ Frequency Ratio for each conditioning parameter, E ¼ number of water body pixels in each landslide’s causative parameter class, F ¼ total number of all well pixels in research region, M ¼ number of pixels in each landslide condition- ing factors, L ¼ total number of all pixels in study area. 3.3. IV model In the current research, the IV model is applied to make GWPZ of the Kohat region. IV is one of the most suitable practices for choosing significant parameters, ranking variables based on their position, and computing their association with inventory GEOMATICS, NATURAL HAZARDS AND RISK 11 data of the study area in the predictive model (Pardeshi et al. 2013). IV model was first improved by Shano et al. (2020). This article considers the IV for each parameter class based on the presence of groundwater inventory pixels in the given region. The computed information value supports governing the role of each parameter class for groundwater occurrence (Ali et al. 2023). The conditional probability was calculated by dividing the groundwater pixels in each parameter class into pixels of a subclass of groundwater parameter, while the prior probability was considered by dividing the total groundwater pixels in the research region by the entire pixels in the whole research region using the Eq. (5) (Pardeshi et al. 2013). MQoxðÞ R MQ xRðÞ o o W ¼ log (5) MQ xRðÞ o o MQ xRðÞ o o W symbolize the weight of parameters for groundwater. MoxðÞ R illustrates water number of pixels within class ‘o’. MQ xMðÞ number of all pixels within class ‘o’, o o MQ xRðÞ total number of water pixels MQ xMðÞ) practice for entire number of O O O i pixels in region. 3.4. Delineation of the GWPZs 3.4.1. Delineation of groundwater using WOE The groundwater potential index (GWPI) was calculated (Eq. (6)) and mapped based on s values. GWPI ¼ s þ s ::: þ s (6) 1 2 n where s is the final weight for the WOE model. 3.4.2. Delineation of groundwater using FR In contrast to the WOE, the weightage of each class in FR is not determined based on the characteristics of the conditioning factor; instead, it is given in the form of the spatial occurrence of the wells in each class. This contrasts with the WOE, which determines the weightage of each class based on the properties of the conditioning factor. Similarly, the FR is computed for each of the conditioning variables. The succeeding scientific Eq. (7) has been applied to produce GWPZ of the Kohat region (Guru et al. 2017). GWPZM ¼ FR (7) ij i¼1 3.4.3. Delineation of groundwater using IV The GWPZ can be produced for Kohat region using the Eq. (8). GWPZ ¼ W þ W þ W þ W þ W þ W þ W þ W þ W þ W (8) E S A C LULC L P F R D 12 F. ISLAM ET AL. W ¼ Weight of Elevation, W ¼ Weight of Slope, W ¼ Weight of Aspect, W E S A C Weight of Curvature, W ¼ Weight of Landuse Landcover, W ¼ Weight of LULC L Lithology, W ¼ Weight of fault, W ¼ Weight of Road, W ¼ Weight of Rainfall, F R P W ¼ Weight of stream network. 3.5. Validation of the GWPM Evaluation of generated GWPZ is crucial because models without validation have no empirical value. Rather than using the hydraulic parameter of specific capacity, as previous studies did, an indirect indicator of groundwater yield measurement was used in the present research (Jha et al. 2010). From a groundwater sustainability point of view, groundwater yield measurement has been used widely by several researchers such as (Qureshi et al. 2010; Pardeshi et al. 2013; Ji et al. 2015; Fayez et al. 2018; Arabameri et al. 2019) for validation of GWPZ. For many investigations, the receiver operating characteristics (ROC) curve has been the gold standard for evaluating the precision of the GWPZ (Shirazi et al. 2012). The area under the AUC measures the accuracy with which a prediction system can determine whether or not an incident will occur (Shah et al. 2022). In order to validate the WOE, RF and IV-generated GWPZ, the healthy dataset (20%) was used for testing. Areas under the ROC curve were used to evaluate the GWPZ, spatial efficacy (AUC). The rate explains the accur- acy with which the model and influencing variables predict the potential. AUC deter- mines which model is superior, and the one with the most outstanding value wins (Rahman 2008). 4. Results In this article, we developed an inventory of groundwater bodies from Sentinel-2, imageries using various advanced JavaScript algorithms, Google Earth Pro and Google Earth images. The spatial location of surface water bodies like well, ponds, and springs is mentioned in Figure 1. In the present research, we accomplished three bivariate models to generate GWPZ for the Kohat area. 4.1. WOE model The contrast value can be computed from the calculation of both mentioned weights and calculate the association of both dependent and independent variables. The con- cluding LSM of the Kohat region is mentioned in Figure 4. Table 2 shows the analyt- ical results of GIS-based models. The two variables’ low correlation shows groundwater’s low potential zone, and the high value illustrates the high groundwater potential zone in the research region. Based on the results of the WOE model in the elevation parameter, an altitude less than 500 m shows a strong association with groundwater. However, more than 800 m elevation class shows the slightest relation- ship with groundwater. GEOMATICS, NATURAL HAZARDS AND RISK 13 Figure 4. The WOE model for groundwater potential zone. 4.2. FR model The ultimate output map by FR is mentioned in Figure 5. Estimating the GWPZ with the FR model cannot be overstated. FR model carried out the GWPZ by correlating the various variables that conditioned the water with the specific locations of bore wells. In addition, a more excellent correlation value suggests a more significant groundwater potential and vice versa. Finally, LULC classes have a significant bearing on the effect of industrialization on the potential of groundwater. According to the findings of this research, the water body was a factor in the highly prospective ability of 9.923. The mining/industrial region and the vegetation cover area both discovered insignificant FR values when the FR was analyzed by the conditioning factor and the bore wells data. This is the case because FR is analyzed. The result suggested a low FR value because more data is needed from these classes’ bore wells. In contrast, the vegetation cover was always found to influence the infiltration rate significantly. The FR model’s elevation class of less than 500 m, as shown in Table 2 in the pre- sent article, illustrates a strong correlation with the groundwater. In contrast, the less correlated elevation class with groundwater is more than 800 m elevation. In the results of the IV model, as shown in Table 2, the most significant elevation for groundwater is <500 m altitude, while the less critical class is more than 800 m. The results revealed that the correlation value of the WOE, FR, and IV model for eleva- tion classes less than 500 m are 1.62, 3.39, and 1.22, respectively. However, the varia- bles association results show that the correlation value of altitude class > 800 m for WOE, FR, and IV model are 1.84, 0.18, and 1.72, respectively. The results of three 14 F. ISLAM ET AL. Table 2. Statistical analysis for GWPZ of District Kohat, Pakistan. No of Landslide %LS No of pixels pixels % Pixels pixels IV ¼ log Parameters Class in class in a class W W WoE in Class in Class (FR) (A/B) Elevation < 500 8360 149 0.58 0.25 0.83 22.16 39.21 1.77 0.57 500–600 16236 138 0.17 0.11 0.28 43.04 36.32 0.84 0.17 600–700 8448 63 1.49 5.51 7.00 22.40 16.58 0.74 0.55 700–800 3234 22 0.40 0.03 0.43 8.57 5.79 0.68 0.39 > 800 1443 8 0.60 0.02 0.62 3.83 20 3.41 0.60 Slope < 5 2211 76 1.11 0.00 0.73 5.87 20 3.41 1.09 10–20 1186 130 2.35 0.00 2.68 3.15 34.21 10.87 2.25 15–25 5998 70 0.61 0.00 2.36 15.92 18.42 1.16 0.60 25–35 11056 61 0.13 0.00 1.22 29.35 16.05 0.55 0.13 > 35 17223 43 1.54 0.00 1.53 45.72 11.32 0.25 1.53 Aspect F 2779 115 1.44 0.29 1.73 1.44 7.37 30.26 4.11 NE 2970 31 0.03 0.00 0.04 0.03 7.87 8.16 1.04 E 5471 42 0.28 0.04 0.32 0.28 14.50 11.05 0.76 SE 5673 49 0.16 0.03 0.18 0.16 15.04 12.89 0.86 S 5416 27 0.71 0.08 0.79 0.71 14.36 7.11 0.49 SW 3920 41 0.04 0.00 0.04 0.04 10.39 10.79 1.04 W 4073 21 0.68 0.06 0.73 0.68 10.80 5.53 0.51 NW 3762 27 0.34 0.03 0.38 0.34 9.97 7.11 0.71 N 3610 27 0.30 0.03 0.33 0.30 9.57 7.11 0.74 Curvature Concave 14057 213 0.4 0.4 0.77 37.27 56.05 1.50 0.41 Flat 6955 117 0.5 0.2 0.69 18.44 30.79 1.67 0.51 Convex 16709 50 1.2 0.4 1.67 44.30 13.16 0.30 1.21 Distance < 200 5359 206 1.36 0.63 2.00 14.27 54.21 3.80 1.33 to Stream 200–400 6009 66 0.08 0.02 0.10 16.01 17.37 1.09 0.08 400–600 6951 45 0.45 0.08 0.53 18.51 11.84 0.64 0.45 600–800 9656 33 1.09 0.21 1.30 25.72 8.68 0.34 1.09 > 800 9069 30 1.13 0.20 1.32 24.16 7.89 0.33 1.12 Precipitation <900 5829 30 0.68 0.09 0.77 15.49 7.89 0.51 0.67 (mm/year) 900–950 10341 62 0.53 0.14 0.67 27.47 16.32 0.59 0.52 950–1000 11035 113 0.01 0.01 0.02 29.32 29.74 1.01 0.01 1000–1050 5823 115 0.68 0.19 0.87 15.47 30.26 1.96 0.67 >1050 4612 60 0.26 0.04 0.30 12.25 15.79 1.29 0.25 LULC Water 200 150 5.69 0.50 6.19 0.53 39.47 74.41 4.31 Trees 337 10 1.10 0.02 1.12 0.89 2.63 2.94 1.08 Crops 3581 52 0.37 0.05 0.42 9.50 13.68 1.44 0.37 Builtup Area 1650 6 1.03 0.03 1.06 4.38 1.58 0.36 1.02 Bare Ground 234 2 0.17 0.00 0.17 0.62 0.53 0.85 0.16 Scrub/Shrub 31700 160 0.70 1.32 2.01 84.08 42.11 0.50 0.69 Lithology Mss 4802 36 0.30 0.04 0.34 12.73 9.47 0.74 0.30 Q 7565 131 0.55 0.20 0.75 20.06 34.47 1.72 0.54 R 16554 175 0.05 0.04 0.09 43.89 46.05 1.05 0.05 Pal 8797 38 0.85 0.16 1.01 23.32 10 0.43 0.85 Fault Buffer <500 1300 40 1.14 0.08 1.21 3.45 10.53 3.05 1.12 1500 2381 62 0.97 0.11 1.08 6.31 16.32 2.58 0.95 3000 3420 40 0.15 0.02 0.17 9.07 10.53 1.16 0.15 5000 4341 45 0.03 0.00 0.03 11.51 11.84 1.03 0.03 >5000 26285 193 0.32 0.49 0.81 69.68 50.79 0.73 0.32 Soil Loamy and 7510 54 0.67 0.64 1.30 53 21 0.71 0.66 shallow soil Mainly 30035 326 0.64 0.67 1.55 27 57 2.69 1.23 loamy soil NDVI Low 15406 117 0.29 0.16 0.45 40.91 40.91 0.01 0.28 High 22252 263 0.16 0.29 0.45 59.09 59.09 0.01 0.16 Distance < 1000 3145 22 0.37 0.03 0.40 8.34 5.79 0.69 0.36 to Road 1000–2000 3993 40 0.01 0.00 0.01 10.58 10.53 0.99 0.01 2000–3000 4328 39 0.11 0.01 0.13 11.47 10.26 0.89 0.11 3000–4000 3569 70 0.68 0.11 0.78 9.46 18.42 1.95 0.67 > 4000 22693 209 0.09 0.12 0.21 60.15 55 0.91 0.09 GEOMATICS, NATURAL HAZARDS AND RISK 15 Figure 5. The FR model for groundwater potential. bivariate models for elevation illustrate that low elevated area is more permeable and suitable for groundwater potential as compared to high elevated zones of Kohat dis- trict. The slope gradient parameter is considered a very significant parameter for GWPZ. The bivariate results achieved from the association of groundwater conditioning parameters and groundwater pixels in the research region, as mentioned in Table 2, revealed that the most critical class for the groundwater potential is 5 followed by 10 –20 . The results also show that the most significant slope class for the current research area is >35 slope followed by 25 –35 . Based on the slope angle parameter, the slope class with less than 5 has the maximum weight. The association rank for both variables in the present work for 5 by WOE, FR, and IV are 2.68, 3,5, and 2.37, respectively. The correlation between water inventory and groundwater condi- tioning parameters for slopes over 35 are 1.45, 0.25, and 1.53 for WOE, FR, and IV models, respectively. 4.3. IV model The analytical values, as shown in Table 2 for the current research analysis between dependent and independent variables. It illustrated that a lower slope has the greatest like- lihood of groundwater potential. In contrast, a steep slope has adverse impacts on the occurrence of groundwater due to high runoff in high slopes region. The analytical results of bivariate models explained that F is the most vital class of aspect, followed by NE of the Kohat area. The correlation results of the WOE, FR, and IV model for the F direction are 16 F. ISLAM ET AL. Figure 6. The IV model for groundwater potential. 1.73, 4.11, and 1.47, respectively. The NE and SW classes of aspects follow the F class of aspects. The less significant class of aspect is the S direction having 0.71, 0.65, and 0.70 for WOE, FR, and IV model. According to the analysis for the association between groundwater data and curvature shape, the concave structure has the highest correlation value, i.e. 0.99, 1.65, and 0.50 for WOE, FR, and IV, respectively. The concave structure is the most significant class of curvature for groundwater potential zone mapping. The results revealed that the spatial association of groundwater and conditioning parameters for WOE, FR, and IV models are 1.67, 0.29, and 1.21, respectively. As shown in Table 2, the results revealed that the most significant class of curvature is a concave struc- ture, followed by Flat and convex structures. As shown in Table 2, the results revealed that groundwater is more likely to occur in a sense stream. There is a maximum likeli- hood of distance from the river of less than 200 m. The correlation value of less than 200 m class of stream is 2.0, 3.80, and 1.33 for WOE, FR, and IV model, respectively. These results illustrate that fewer distances to rivers have had a more significant influence on groundwater potential (Figure 6). However, most of the less significant class of stream parameters are greater than 800 m, followed by a 600 m–800m range. The association between WOE, FR, and IV variables are 1.32, 0.33, and 1.12, respectively, for more significant than 800 m class of stream. In the current research, the precipitation map was formed from CHIRPS sat- ellite data, followed by the reclassification into five categories to assess the relationship of rainwater factor with groundwater bodies. The outcomes, as revealed in Table 2 for rainfall, supported that rainfall is a significant aspect of groundwater potential. The results show that the 1000–1050 mm/year precipitation class is the most significant for groundwater potential, followed by >1050mm/year. The 1000–1050 class correlation GEOMATICS, NATURAL HAZARDS AND RISK 17 between rainfall and groundwater is 1.32, 2.73, and 1.0 for WOE, FR, and IV models. The precipitation class < 900 mm/year has no significant impact on groundwater poten- tial. The bivariate analysis for WOE, FR, and IV are 0.90, 0.45, and 0.79, respect- ively. Considering the above-mentioned statistical facts, it can be concluded that high precipitation classes show more groundwater occurrence and vice versa. In the current research results, the cropland area is the most important and influential class of LULC parameter in the study area. The analytical results of groundwater and conditioning parameters for WOE, FR, and IV are 0.63, 1.70, and 0.53, respectively. The agricultural land shows high potential results for groundwater because the agriculture region is recharged from the irrigation system of the study area. The scrub/shrub, forest, and urban class of LULC follow the agricultural land. The analysis of both variables treasures that Q is the most influential geological forma- tion of lithology parameters for groundwater potential in the Kohat region. The correl- ation of both variables is clear in WOE, FR, and IV model. The results for groundwater potential zone mapping of the present study show that the lithological parameter is the least significant class for groundwater. A fault is a significant parameter for groundwater percolation. Geological faults strongly influence groundwater mobility because they enhance the strata’s mobility mechanism for groundwater. The maximum likelihood of groundwater potential in distances to a fault is <500 m buffer region. The correlation value of both variables of class <500 m for WOE, FR, and IV are 0.60, 1.73, and 0.55, respectively. The geological fault’s > 5000m fault buffer has no significant impact on groundwater. The results of >5000 m buffer for the WOE, FR, and IV model are 0.45, 0.82, and 0.20, respectively. The results concluded that fault is the influential parameter for groundwater potential for the present research area. As shown in Table 2, the results explained that mainly loamy soil is the suitable class for groundwater in the Kohat area. The correlation of both variables for WOE, FR, and IV are 1.55, 2.69, and 1.23, respect- ively. As shown in Table 2, the present research results illustrate that NDVI is a crucial parameter for groundwater potential. Both variables’ association ranks are 0.57, 1.83, and 0.27 for WOE, FR, and IV model, respectively. The results of the present study between NDVI and GWPZ revealed that NDVI and groundwater Table depth are inversely related, i.e. high NDVI will have low water table depth and vice versa for the current investigation. The current study considered the road to compute the association between the road net- work in the Kohat region and the groundwater potential. The results explained that the most influential class of road network is a 3000–4000 m buffer followed by >4000 m and 2000–3000 m buffer. The correlation value for WOE, FR, and IV model are 0.78, 1.95, and 0.67, respectively. Table 2 revealed that the <1000 m class correlation values between the road and groundwater are 0.40, 0.69, and 0.36 for WOE, FR, and IV model, respectively. The results of all road network buffers revealed that the road network has adverse impacts on groundwater in the study area. 4.4. Validation 4.4.1. Validation of models In the modelling technique, validation of the model is a significant phase to accom- plish the reliable scientific worth of the research project (Barakat et al. 2023). In 18 F. ISLAM ET AL. Figure 7. ROC curves for WOE, FR, and IV methods. numerous research, the AUC technique was used to evaluate GWPZ. This ROC curve is considered a standard index for accuracy assessment. This technique has been extensively utilized for assessing techniques applied in various water research investi- gations. The receiver operating characteristics (ROC) graph validated the WOE, FR, and IV models. The region indicates the precision of the prediction or classification under the receiver operating characteristic curve (AUC) (Pourghasemi and Rossi 2017). In this investigation, we have tested and verified three different models derived from the GWPZ’s final categorization. The AUC values range from 0 to 1. If the number is less than 0.5, the model’s classification was inappropriate, and it should be redone. On the other hand, if the value is close to 1, it suggests that the result is clearly defined (Pourghasemi and Rossi 2017). To verify WOE, FR, and IV models, the ROC curves of the GWPZ maps were constructed (Figure 7). The finding demonstrates that the outcome predicted by the FR model for GWPZ (AUC ¼ 91%) is successfully achieved when compared to both the WOE model (AUC ¼ 88%) and the IV model (AUC ¼ 89%). Nevertheless, all the obtained findings were checked for validity and clearly defined (Pourghasemi and Rossi 2017). However, the FR is a better representative for this study area to indicate the spatial distribution of the GWPZ compared to the WOE and FR models. This is because the GWPZ is more likely to be found in areas where the FR model is more accurate. As a result, this validation method is highly recommended for research into potential groundwater evaluation. The generated validation graphs of the applied models in the present study, as mentioned in Figure 7, utilized twenty percent of the inventory data of water. The highest value of the AUC value showed the most reliable results of the model and while the lowest value showed unreliable results. The find- ings by mentioned validation technique for WOE, FR, and IV clearly explained that all applied models are consistent and trustworthy methods to produce GWPZ for the GEOMATICS, NATURAL HAZARDS AND RISK 19 Table 3. Accuracy assessment of land use and land cover. S. No Classes UA PA OA K 1 Water 90.00 91.84 87.00 0.81 2 Trees 88.00 84.62 3 Crops 86.00 87.76 4 Built up area 92.00 86.79 5 Bare ground 86.00 91.49 6 Shrubs 88.00 88.00 Notes: UA ¼ User’s Accuracy, PA ¼ Producer’s Accuracy, OA ¼ Overall Accuracy, K ¼ Kappa Coefficient. Kohat District of Pakistan. The validation outcome specifies that the FR technique is the most reliable method for GWPZ in the study area. 4.4.2. LULC accuracy assessment Accuracy evaluations were performed after obtaining land use/land cover categoriza- tion outcomes. In order to do so, we used the user accuracy matrix, the producer accuracy matrix, and the total accuracy matrix to measure precision. They calculated the users’ accuracy by taking the ratio of adequately classified cells to the total num- ber of reference points. Google Earth was used as a reference tool for this research. The overall accuracy was calculated by dividing properly classified cells by all pixels. In contrast, producer accuracy was calculated by dividing the number of cells with correct land use/land cover classification by the number of ground truth pixels as explained in Table 3. 5. Discussion Due to the increased demand for water availability for urbanization, industrialization, and irrigation purposes, there has been an increase in the research investigation on the groundwater scenario. This is especially true in arid to semi-arid regions world- wide, where the need for groundwater is even more critical. There have been numer- ous research to understand the science behind water recharge and prepare GWPZ for the scientific exploration and management of groundwater (Arabameri et al. 2019). Therefore, proper groundwork and methods should be implemented for GWPZ to manage the groundwater because the execution method for GWPM is still an argued subject (Nampak et al. 2014; Park et al. 2014). This article has emphasized the appreciation of the groundwater potential of the Kohat region of Pakistan has been evaluated using WOE, FR, and IV models. These models were applied to compute the correlation between water body pixels and con- ditioning parameters for groundwater. The lower correlation value represents low potential zones, while the high correlation shows high potential groundwater regions. The probability of groundwater potential generally diminutions with increasing eleva- tion (S. Hasan AL-Zuhairy et al. 2017). In the present study, the spatial analysis dis- closed that the elevation class of < 500 m has a higher correlation value between both variables; however, the > 800 m elevation class revealed no significant associ- ation for groundwater potential. The slope gradient is an influential parameter for groundwater potential because steep slopes are the significant parameter in GWPZ. Moreover, the slope gradient is another significant parameter for groundwater 20 F. ISLAM ET AL. potential. A steep slope gradient adversely impacts on groundwater because it increases the surface run-off and affects the intrusion of precipitation into the ground (Jaiswal et al. 2003). If the slope angle is greater than 35 , groundwater potential is reduced because it restricts the aquifer’s recharge (Madrucci et al. 2008). The current study considers slope angle a critical factor for GWPZ. The association of dependent and independent variables for slope up to 20 is very suitable for the high potential zone of groundwater. In contrast, the slope angle > 35 has an inverse relationship with the groundwater pixel and revealed low groundwater potential, as shown in Table 2 of the results. The flat surface of the aspect is more appropriate for ground- water amount (Manap et al. 2013). In the present study, we observed that the flat surface of the aspect has a strong association with groundwater inventory data. The flat surface correlates with 0.86, 1.97, and 0.68 using WOE, FR, and IV models. The FR and EBF model revealed that concave and convex structures are less associated with groundwater potential than flat regions. Water reservoir and aquifer recharge mainly occurred in the flat region; however, the convex and concave structures did not support the water storage and infiltration (Arabameri et al. 2020). In this study, our results concluded that the Flat class of curvature strongly correlates with groundwater, followed by Concave. At the same time, convex adversely impacts groundwater potential, as shown in Table 2. The most developed likelihood of groundwater is perceived in denser drainage networks. In the current investigation, the < 200 m class of drainage network shows the most influential association with groundwater potential using WOE, FR, and IV technique, followed by 200–400 m and 400–600 m. The relationship ranks of the> 800 m class of drainage revealed that this class has no impact on groundwater potential. The rainfall strongly correlates positively with aquifer recharge (Wu et al. 2020). In this study, the precipitation class 1000–1050mm/year strongly correlates with groundwater potential having a positive correlation y followed by >1050 mm/year. The low precipitated area has no inverse relationship with the groundwater potential of the study area. The pre- cipitation class <900mm/year has minor importance for groundwater potential in the current study area and is followed by 900–950 mm/year Crops and a garden class of the LULC parameter are significantly associated with groundwater having correlation values of 2.06 and 1.25, respectively, demonstrating these classes’ high potential water zones (Falah et al. 2017). In the context of binary classification, the Receiver Operating Characteristic (ROC) curve is a popular method to evaluate and compare the performance of different models. The ROC curve plots the True Positive Rate (TPR) against the False Positive Rate (FPR) for different thresholds of a model’s predicted probability (Li et al. 2021). A model with a higher AUC (Area Under the ROC Curve) is considered better. In this study we used WOE, FR and IV and compare their performance using the ROC curve. Model WOE has an AUC of 88%. This means that it has a good balance between TPR and FPR, with relatively few false positives and false negatives. Model WOE is likely to be a good choice for classification tasks where both precision and recall are important (Wahla et al. 2022). Model FR has an AUC of 0.91. This means that it has a high TPR and low FPR, making it suitable for applications where identifying true positives is crucial, and false GEOMATICS, NATURAL HAZARDS AND RISK 21 positives are less of a concern. However, Model IV may be too aggressive in classify- ing examples as positive, leading to a high false-negative rate. Model IV has an AUC of 91%. This means that it has a higher FPR and lower TPR compared to Model A, but still performs better than random guessing. Model IV may be useful in cases where minimizing false positives is critical, but it may not perform as well in cases where false negatives are costly. In summary, each model has its strengths and weaknesses, and the choice of the appropriate model depends on the specific requirements of the task at hand. Model FR strikes a good balance between TPR and FPR, Model IV is useful when minimiz- ing false positives is crucial, and Model WOE is suitable for identifying true positives at the expense of false negatives. Our research results in the Kohat region of Pakistan showed that cropland is the most influential factor for groundwater potential. The correlation value for cropland in the current research are 0.63, 1.70, and 0.53 for WO, FR, and IV, respectively. Concerning the geological fault buffer, it was hypothesized that the association between both variables for groundwater would weaken the further away from the fault one got. Their relationship increases when the distance from the fault decreases (Falah et al. 2017). Our present study results in the Kohat area presented that fault favours water infiltration and supports the aquifer recharge in the current area. The most effective fault buffer is <500 m because this class shows a strong positive correl- ation of 0.60, 1.73, and 0.55 applying the WOE, FR, and IV model, followed by 1500 m and 3000 m buffers. However, the buffer of >5000 class has no significant role in groundwater potential and recharge of water. The NDVI is a vital parameter for groundwater potential. NDVI and water Table have an inverse relationship, i.e. when the NDVI increases, the water table rise and vice versa (Seeyan et al. 2014). The same scenario we observed in our current research region. The high NDVI zone strongly correlates with groundwater, while the low NDVI region adversely impacts the present area. As shown in Table 2, the results justified the above statement for NDVI association with groundwater. According to the analytical results in Table 2, drainage network,slope,elevation,and rain- fall are the most significant parameters for GWPZ in the present research area. According to GIS-based statistical models, the FR is the best technique for GWPZ in the current research project. Final GWPZ was also produced using GIS-based models and then was classified into five classes of very low, low, moderate, high, and very high groundwater potential zones. The final GWPZ can be helpful for various research organizations like agriculture and energy-related sectors to manage the groundwater in the present study area. 6. Conclusions This article describes a study that aims to investigate potential groundwater zones in the Kohat District of Pakistan using three different GIS-based models: Weight of Evidence (WOE), Frequency Ratio (FR), and Information Value (IV). The study uses various data sources, including satellite imagery, ground surveys, and public health department data, to develop an inventory map of groundwater and twelve ground- water conditioning parameters. The study then applies the three GIS-based models to 22 F. ISLAM ET AL. generate GWPZ maps and categorizes them into five categories based on their poten- tial for groundwater availability. The study finds that stream, slope angle, elevation, and rainfall are the most significant parameters for GWPZ. The study uses ROC curves to assess the accuracy of the models and finds that FR is the most reliable model for the study. The study concludes that the GWPZ maps generated by the WOE, FR, and IV techniques can be useful for research and development agencies to improve groundwater exploration and development planning in the future. Acknowledgements The authors would like to thank the university authority for financial support. The authors thanks to (TURSP-2020/82), Taif University, Taif, Saudi Arabia. Author contributions Fakhrul Islam: methodology, software, formal analysis, visualization, data curation, writing— original draft, investigation, validation, writing—review and editing, Aqil Tariq: formal ana- lysis, visualization, data curation, writing—review and editing, Supervision. Rufat Guluzade: writing—review and editing. Na Zhao: Funding, writing review and editing, Safeer Ullah Shah: data curation, writing—original draft, investigation, validation, writing—review and edit- ing, Matee Ullah: writing—review and editing. Mian Luqman Hussain: writing—review and editing. Muhammad Nasar Ahmad: writing—review and editing, Abdulrahman Alasmari: writing—review and editing, Fahad M. Alzuaibr: writing—review and editing, Ahmad El Askary: writing—review and editing, Muhammad Aslam: writing—review and editing. All authors have read and agreed to the published version of the manuscript. Funding The Key Project of Innovation LREIS (KPI001). The authors would like to thank the university authority for financial support. The authors thanks to (TURSP-2020/82), Taif University, Taif, Saudi Arabia. ORCID Aqil Tariq http://orcid.org/0000-0003-1196-1248 Abdulrahman Alasmari http://orcid.org/0000-0003-1212-8581 Data availability statement st Data available on the reasonable request from the 1 author of this article. Disclosure statement No potential conflict of interest was reported by the authors. GEOMATICS, NATURAL HAZARDS AND RISK 23 References Ahmad I, Dar MA, Teka AH, Teshome M, Andualem TG, Teshome A, Shafi T. 2020. GIS and fuzzy logic techniques-based demarcation of groundwater potential zones: a case study from Jemma River basin, Ethiopia. J African Earth Sci. 169:103860. Ahmad MN, Shao Z, Aslam RW, Ahmad I, Liao M, Li X, Song Y. 2022. 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Journal
Geomatics Natural Hazards and Risk
– Taylor & Francis
Published: Dec 31, 2023
Keywords: Ground water potential zones; GIS; RS; FR; AUC
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References
GIS and fuzzy logic techniques-based demarcation of groundwater potential zones: a case study from Jemma River basin, Ethiopia
Landslide hazard, susceptibility and risk assessment (HSRA) based on remote sensing and GIS data models: a case study of Muzaffarabad Pakistan
Spatial downscaling of GRACE data based on XGBoost model for improved understanding of hydrological droughts in the Indus Basin Irrigation System (IBIS)
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