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Zoning the land-capability of Roudehen for agricultural usage by the OWA1 technique in geographic information system environment

Zoning the land-capability of Roudehen for agricultural usage by the OWA1 technique in geographic... GeoloGy, ecoloGy, and landscapes, 2018 Vol . 2, no . 1, 1– 7 https://doi.org/10.1080/24749508.2017.1389497 INWASCON OPEN ACCESS Zoning the land-capability of Roudehen for agricultural usage by the OWA1 technique in geographic information system environment a b Farah Tavana Mehrabani and Ahmad Nohehgar a b Management of natural disasters, University of Tehran, Tehran, Iran; Faculty of environmental s cience, University of Tehran, Tehran, Iran ABSTRACT ARTICLE HISTORY Received 27 July 2017 One of the most sensitive issues that must be considered in many decisions are environmental a ccepted 17 s eptember 2017 factors. Planning for the optimal use of land makes it possible to maintain natural resources for using in future by the Earth too. Locating is one of the most widely used spatial decision- KEYWORDS making which can be influenced by many environmental factors. The aim of the locating is environmental factors; to find a set of appropriate spatial options for a particular application. The locating issue is a locating; land power; multi-criteria decision-making problem. In current article, we have used OWA land-capability prevention; oW a method classification using GIS in order to prepare, evaluate, classify and overlay the layers. We have used six parameters of height, land use, climate, soil, slope and tilt direction for evaluating and categorizing land power. Finally, the areas that indicates the best potential of farming and other classes have been identified. 1. Introduction 205 Technical Journal. Karamiyan, Onaq, and Payamati (2008). In his research entitled “Land Use Management Any exploitation of the land that we are above capable of Program of Kouhdasht Lorestan,” has used land survey- in the long run will destroy and reduce fertility. Thus, it us ing for studying and adapting the current status of land vital to recognize land production capacity and allocate use planning (capacities or capability) and had concluded it to the best and most prot fi able type of using. Planning that in 34% of the area of this area are work usage. There is for optimal land usage makes it possible to preserve nat- a slight correlation between these two factors. In the mid- ural resources for future use by the Earth too (Leopold, 1990s, the FAO introduced a method which was called 1968). To do this, land resources must be identified first “AEZ-zoning,” whereby the Earth’s surface, according to and their capacities and capabilities must be determined physical parameters, has divided into more or less homo- for possible uses. This kind of studies can be made in geneous regions for the production of important crop the form of land plot plans too, because in discussing products. In current study, the modified Mahler method land management and the discussion is about the proper (evaluation of Iranian lands) has been used for calibrating use of resources and land, which is necessarily a kind of the physical and climatic conditions of Iran in order to adaptation of suitable land usage and is a special advan- obtain more accurate results (Tavakkol, 1997). tage to this. It is according to a quantitative assessment Decision-making can be considered as the vital chal- based on expert judgement and a quantitative assessment lenge facing experts and analysts to solving a variety of according to the predetermined stages (model) (Van problems. Therefore, various methods and algorithms Beek, 2009). Murphy (2004) studied the land and land have been indicated for supporting the decision-m aking suitability in veils, and in his research he had studied the over the last few decades. Multi-criteria decision-m aking impacts of work and land management on the soil, where issues usually consists of a set of location that must be the adaptation among soil and its properties is low for according to several criteria in GIS as a process that the national parks and protected areas, and recommend determines spatial data (maps) and valuation values wherever it is adapted to agriculture and rangelands. The (priorities and criteria of analysts). In better words, the evaluation history in Iran dates back to 1935. To achieve MCDA suggests a specific model to optimize spatial this aim, a recipe had been prepared by Iranian experts decision-making, and many studies have been done so and FAO experts, including Mahler, which had been used far. Heywood, Oliver, and Tomlinson (1995) recom- until 1949, and in the year 1949, the recipe had been mended multi-criteria analyses in GIS, it consists of a labelled with guides Land classification had published comparison of the results obtained from various deci- by the Soil and Water Research Institute, known as the sion rules. CONTACT Farah Tavana Mehrabani farahtavana1234@gmail.com © 2018 The a uthor(s). published by Informa UK limited, trading as Taylor & Francis Group. This is an open a ccess article distributed under the terms of the creative c ommons a ttribution 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. 2 F. TAVANA MEHRABANI AND A. NOHEHGAR Figure 1. l ocation of the studied area. Table 1. The classes of various parameters and their coefficients. methods, have been used in several locational and land s oil 16 ‘Val Ue’ (1 1; 2 2; 3 3) use assessment problems too (Beedasy & Whyatt, 1999). climate 16 ‘V al Ue’ (1 5; 2 4; 3 3; 4 2; 5 1) es Th e methods have also combined with the weighted land use 18 ‘V al Ue’ (1 1; 2 2) average weighting principles in various applications 20 Height ‘Val Ue’ (1 5; 2 4; 3 3; 4 2; 5 1) 12 direction ‘Val Ue’ (1 4; 2 3; 3 2; 4 1) (Makropoulos, Butler, & Maksimovic, 2003). 18 slope ‘Val Ue’ (1 8; 2 7; 3 6; 4 5; 5 4; 6 3; 7 2; 8 1) 1 9 1 e o Th utcomes obtained from the application of mul- ti-criteria evaluation methods, that include boolean overlapping operators and weighted linear constit- Table 2. c ombination of classes and coefficients. uents, can be determined by the use of the weighted average weighted average (OWA) method that had Criteria (parame- Row ters) Coefficients Classes been improved. Conventional and traditional methods 1 s oil 16 1:1; 2:2; 3:3 of OWA have been used in many GIS spatial applica- 2 climate 16 1:1; 2:2; 3:3; 4:4; 5:5 tions (Calijuri, Marques, Lorentz, Azevedo, & Carvalho, 3 land usage 18 1; 2 4 Height 20 1:5; 2:4; 3 2004). OWA is a family of multi-criteria combination 5 direction 12 1:4; 2:3; 3:2; 4:1 methods (Yager, 1988). 6 slope 18 1:8; 2:7; 3:6; 4:5; 5:4; This method consists of two categories of weights: 6:3; 7:2; 8:1 weights belongs to relative importance of criteria and sequential weights. Rinner and Malczewski have devel- Jankowski, Andrienko, and Andrienko (2001) used oped the ability of supporting the decision-making by the multi-criteria DECADE/Common GIS analysis on adding an OWA module to Common GIS. Thou, the decision-wing search and discovery stressed. studies have indicated that conventional OWA methods It is notable that, many studies have been done on have limited impacts when they have broad evaluation multivariate statistics in multi-criteria evaluation meth- criteria. In these situations, the combination of criteria ods too (Andrienko, Andrienko, & Jankowski, 2003). in such a way that assumptions are decisive will be very Providing location mapping and evaluating various complicated. areas is one of the most useful GIS applications for spatial For many criteria, the person encounters the issue of planning and management (Collins, Steiner, & Rushman, combining the criteria maps to the extent that their out- 2001). During the last decade, the issue of locating suit- comes meet the priorities. In such cases, the key aspects able applications has been used in multi-criteria evalua- of the decision issue may be indicated in terms of some tion methods in GIS increasingly, (Barredo, Benavides, fuzzy conceptual quantities such as “Most criteria must Hervás, & van Westen, 2000; Dai, Lee, & Zhang, 2001; be estimated” or “80% of the criteria must be met.” This Joerin, Thériault, & Musy, 2001). The conventional needs a change in the multi-criteria decision-making methods of multi-criteria analysis in GIS, like boolean methods, thus the conditions of these fuzzy quantities overlapping operators and weighted linear composition can be met (Boroushaki & Malczewski, 2008). GEOLOGY, ECOLOGY, AND LANDSCAPES 3 Figure 2. Height classes map. Figure 3. land use map. its area is over 200 km . The area is between 55 and 51 2. Material and methods and latitude 43 and 35. More precisely, the northern First of all, the natural features of the Roudehen area had boundary is formed by the mountain ridge of the north been studied. This study consist of topographic, slope, cli - of Lavasan to the hill of Emamzadeh Hashem. From the matic characteristics, soil talent, land use and orientation south, the mountains of Quch mountain, Suri Qal’eh and the area of Roudehen in terms of agricultural land by and … have separated this region from the central considering the OWA method has been studied in GIS. region of Iran. It has mild climate. Roudehen section Case Study of Area: Roudehen district, Damavand is in north of Amol city in Mazandaran province, and city, Tehran province. from the east it has neighbourhood with central part of Damavand, and from the west it has neighbourhood 2.1. Introducing the Roudehen section with the central part of Tehran (with Boumehen and Pardis town) and from the south it has neighbourhood The Roudehen section is 35 km North-east of Tehran with the mountains and Varamin and Pakdasht deserts province and in the foothills of southern Alborz south- (Figure 1). ern wall. The average height of the town is 1850 m and 4 F. TAVANA MEHRABANI AND A. NOHEHGAR Figure 4. climate classes. Figure 5. sloping classes map. 2.2. Research methodology [0,1] = W for j = 1, 2, …, n with map layers and stand- IJ ard weights, the OWA combination operator to the cell 2.2.1. OWA location i is a set of sequential weights v  = v , v , …, v 0 1 2 n Multi-criteria evaluation methods in GIS usually con- assigns, since for each j = 1, 2, …, n we will have. The sist of a set of spatial assessment criteria in the form of OWA combination operator is defined as follows (Yager, maps and layers. But the problem that normally hap- 1988): pens in spatial decision-making is how to combine the � � criteria maps with a set of descriptive values (weights) � u v j j OWA = z beside decision-makers’ preferences. Spontaneous deci- ∑ i n ij u v j=1 j=1 j j sion-making must lead to the selection of one (or mul- tiple options are spatial options. Each of these options, where in that z ≥ z ≥ ⋯ ≥ z by sorting the i1 i2 in (i = 1, 2, …, m), is described with a standardized set of a , a ,… , a i1 i2 in values (a ). The multi-criteria evaluation question con- ij Descriptive values are achieved and u is the same sists of a priority set as a weight of the criteria too, weight criterion that is sorted due to the order of z . As ij you can see, two types of weight have been used in this a ∈ [0, 1] for j = 1, 2, … , n ij GEOLOGY, ECOLOGY, AND LANDSCAPES 5 Figure 6. s oil classes. Figure 7. slope classes map. method: critical weights and sequential weights. The in relation to evaluation criteria. For instance, the fol- weight of the critical indicates the relative importance of lowing criteria can be combined: “More criteria are met,” each evaluation criteria (layers and maps), but sequential “All criteria must be met,” “At least half of the criteria weights are allocated according to the location of the cells are met.” of the layers and trays. It means that all cells in the same Such kinds of methods are called multi-criteria eval- position in several criteria maps will have the same com- uation by fuzzy quantizers (Yager, 1988). This method bined weights. Thus, all cells share a common weight in a consists of three main steps: (1) determining the type of nap, but there are varieties in their sequential weight. The the Q quantizer; (2) producing a set of sequential weights equation may be the same as the “weighted linear compo- related to Q; and (3) calculating and evaluating the posi- sition” in which the standard weights have been changed. tion of each of the cells using the combinative function OWA combination with quantitative fuzzy concepts. of OWA (Boroushaki & Malczewski, 2010). With a set of criteria maps and a quantizing fuzzy Case Study Parameters: Slope, direction, elevation, concept, Q can be used to combine maps with a “phrase” climate, soil and land use (Tables 1 and 2). 6 F. TAVANA MEHRABANI AND A. NOHEHGAR Figure 8. Map of agricultural power classes based on the method of regular average weighing. Boroushaki, S., & Malczewski, J. (2010). Using the fuzzy 3. Results majority approach for GIS-based multicriteria group See Figures 2–8. decision-making. Computers & Geosciences, 36(3), 302–312. Calijuri, M.L., Marques, E.T., Lorentz, J.F., Azevedo, R.F., & Carvalho, C.A. (2004). Multi-criteria analysis for the 4. Conclusion identification of waste disposal areas. Geotechnical and Geological Engineering, 22(2), 299–312. In current research, we have used GIS for preparing, Collins, M.G., Steiner, F.R., & Rushman, M.J. (2001). Land- evaluating, classifying and overlapping layers. We have use suitability analysis in the United States: Historical used six parameters for evaluating and classifying the development and promising technological achievements. power of the land. Finally, the areas that indicate the Environmental Management, 28(5), 611–621. Dai, F.C., Lee, C.F., & Zhang, X.H. (2001). GIS-based geo- best potential for farming and other classes are iden- environmental evaluation for urban land-use planning: A tified. Totally, using this technique, we are able to case study. Engineering Geology, 61(4), 257–271. evaluate the potential of the land for a variety of uses, Joerin, F., Thériault, M., & Musy, A. (2001). Using GIS and but there are more precise techniques that we recom- outranking multicriteria analysis for land-use suitability mend, the multi-criteria decision-making techniques assessment. International Journal of Geographical Information Science, 15(2), 153–174. combined with fuzzy functions must be used in next Jankowski, P., Andrienko, N., & Andrienko, G. (2001). Map- researches. centred exploratory approach to multiple criteria spatial decision making. International Journal of Geographical Information Science, 15(2), 101–127. Disclosure statement Heywood, I., Oliver, J., & Tomlinson, S. (1995). Building No potential conflict of interest was reported by the authors. an exploratory multi-criteria modelling environment for spatial decision support. Innovations in GIS, 2, 127–136. Karamiyan, R., Onaq, M., & Payamati, K. (2008). Water References management of kouhdasht of lorestan using territorial Andrienko, G., Andrienko, N., & Jankowski, P. (2003). planning. Journal of Agricultural Science and Natural Building spatial decision support tools for individuals and Resources, 15(2), 183–193. groups. Journal of Decision Systems, 1(2), 193–208. Leopold, L.B. (1968). Hydrology for urban land planning: Barredo, J., Benavides, A., Hervás, J., & van Westen, C.J. A guidebook on the hydrologic effects of urban land use. (2000). Comparing heuristic landslide hazard assessment Washington, DC: US Geological Survey. techniques using GIS in the Tirajana basin, Gran Canaria Makropoulos, C.K., Butler, D., & Maksimovic, C. (2003). Island, Spain. International Journal of Applied Earth Fuzzy logic spatial decision support system for urban Observation and Geoinformation, 2(1), 9–23. water management. Journal of Water Resources Planning Beedasy, J., & Whyatt, D. (1999). Diverting the tourists: A and Management, 129(1), 69–77. spatial decision-support system for tourism planning on Murphy, B.W. (2004). A revised land and soil capability a developing island. International Journal of Applied Earth classic fi ation for New South Wales . 13t International Soil Observation and Geoinformation, 1(3–4), 163–174. Conservation Organization Conference-Brisbane. Boroushaki, S., & Malczewski, J. (2008). Implementing an Tavakkol, M.S. (1997). e Th necessity of environmental extension of the analytical hierarchy process using ordered assessment of land in physical development plans. Journal weighted averaging operators with fuzzy quantifiers in of Environmental studies, University of Tehran, 18(18), ArcGIS. Computers & Geosciences, 34(4), 399–410. 61–74. GEOLOGY, ECOLOGY, AND LANDSCAPES 7 Van Beek, E. (2009). Managing water under current climate Yager, R.R. (1988). On ordered weighted averaging variability. In F. Ludwig, P. Kabat, H. van Schaik, & M. aggregation operators in multicriteria decisionmaking. Micheal van der valk (Eds.), Climate Change Adaptation in IEEE Transactions on Systems, Man, and Cybernetics, the Water Sector (pp. 51–77). London: Earthscan. 18(1), 183–190. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geology Ecology and Landscapes Taylor & Francis

Zoning the land-capability of Roudehen for agricultural usage by the OWA1 technique in geographic information system environment

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Abstract

GeoloGy, ecoloGy, and landscapes, 2018 Vol . 2, no . 1, 1– 7 https://doi.org/10.1080/24749508.2017.1389497 INWASCON OPEN ACCESS Zoning the land-capability of Roudehen for agricultural usage by the OWA1 technique in geographic information system environment a b Farah Tavana Mehrabani and Ahmad Nohehgar a b Management of natural disasters, University of Tehran, Tehran, Iran; Faculty of environmental s cience, University of Tehran, Tehran, Iran ABSTRACT ARTICLE HISTORY Received 27 July 2017 One of the most sensitive issues that must be considered in many decisions are environmental a ccepted 17 s eptember 2017 factors. Planning for the optimal use of land makes it possible to maintain natural resources for using in future by the Earth too. Locating is one of the most widely used spatial decision- KEYWORDS making which can be influenced by many environmental factors. The aim of the locating is environmental factors; to find a set of appropriate spatial options for a particular application. The locating issue is a locating; land power; multi-criteria decision-making problem. In current article, we have used OWA land-capability prevention; oW a method classification using GIS in order to prepare, evaluate, classify and overlay the layers. We have used six parameters of height, land use, climate, soil, slope and tilt direction for evaluating and categorizing land power. Finally, the areas that indicates the best potential of farming and other classes have been identified. 1. Introduction 205 Technical Journal. Karamiyan, Onaq, and Payamati (2008). In his research entitled “Land Use Management Any exploitation of the land that we are above capable of Program of Kouhdasht Lorestan,” has used land survey- in the long run will destroy and reduce fertility. Thus, it us ing for studying and adapting the current status of land vital to recognize land production capacity and allocate use planning (capacities or capability) and had concluded it to the best and most prot fi able type of using. Planning that in 34% of the area of this area are work usage. There is for optimal land usage makes it possible to preserve nat- a slight correlation between these two factors. In the mid- ural resources for future use by the Earth too (Leopold, 1990s, the FAO introduced a method which was called 1968). To do this, land resources must be identified first “AEZ-zoning,” whereby the Earth’s surface, according to and their capacities and capabilities must be determined physical parameters, has divided into more or less homo- for possible uses. This kind of studies can be made in geneous regions for the production of important crop the form of land plot plans too, because in discussing products. In current study, the modified Mahler method land management and the discussion is about the proper (evaluation of Iranian lands) has been used for calibrating use of resources and land, which is necessarily a kind of the physical and climatic conditions of Iran in order to adaptation of suitable land usage and is a special advan- obtain more accurate results (Tavakkol, 1997). tage to this. It is according to a quantitative assessment Decision-making can be considered as the vital chal- based on expert judgement and a quantitative assessment lenge facing experts and analysts to solving a variety of according to the predetermined stages (model) (Van problems. Therefore, various methods and algorithms Beek, 2009). Murphy (2004) studied the land and land have been indicated for supporting the decision-m aking suitability in veils, and in his research he had studied the over the last few decades. Multi-criteria decision-m aking impacts of work and land management on the soil, where issues usually consists of a set of location that must be the adaptation among soil and its properties is low for according to several criteria in GIS as a process that the national parks and protected areas, and recommend determines spatial data (maps) and valuation values wherever it is adapted to agriculture and rangelands. The (priorities and criteria of analysts). In better words, the evaluation history in Iran dates back to 1935. To achieve MCDA suggests a specific model to optimize spatial this aim, a recipe had been prepared by Iranian experts decision-making, and many studies have been done so and FAO experts, including Mahler, which had been used far. Heywood, Oliver, and Tomlinson (1995) recom- until 1949, and in the year 1949, the recipe had been mended multi-criteria analyses in GIS, it consists of a labelled with guides Land classification had published comparison of the results obtained from various deci- by the Soil and Water Research Institute, known as the sion rules. CONTACT Farah Tavana Mehrabani farahtavana1234@gmail.com © 2018 The a uthor(s). published by Informa UK limited, trading as Taylor & Francis Group. This is an open a ccess article distributed under the terms of the creative c ommons a ttribution 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. 2 F. TAVANA MEHRABANI AND A. NOHEHGAR Figure 1. l ocation of the studied area. Table 1. The classes of various parameters and their coefficients. methods, have been used in several locational and land s oil 16 ‘Val Ue’ (1 1; 2 2; 3 3) use assessment problems too (Beedasy & Whyatt, 1999). climate 16 ‘V al Ue’ (1 5; 2 4; 3 3; 4 2; 5 1) es Th e methods have also combined with the weighted land use 18 ‘V al Ue’ (1 1; 2 2) average weighting principles in various applications 20 Height ‘Val Ue’ (1 5; 2 4; 3 3; 4 2; 5 1) 12 direction ‘Val Ue’ (1 4; 2 3; 3 2; 4 1) (Makropoulos, Butler, & Maksimovic, 2003). 18 slope ‘Val Ue’ (1 8; 2 7; 3 6; 4 5; 5 4; 6 3; 7 2; 8 1) 1 9 1 e o Th utcomes obtained from the application of mul- ti-criteria evaluation methods, that include boolean overlapping operators and weighted linear constit- Table 2. c ombination of classes and coefficients. uents, can be determined by the use of the weighted average weighted average (OWA) method that had Criteria (parame- Row ters) Coefficients Classes been improved. Conventional and traditional methods 1 s oil 16 1:1; 2:2; 3:3 of OWA have been used in many GIS spatial applica- 2 climate 16 1:1; 2:2; 3:3; 4:4; 5:5 tions (Calijuri, Marques, Lorentz, Azevedo, & Carvalho, 3 land usage 18 1; 2 4 Height 20 1:5; 2:4; 3 2004). OWA is a family of multi-criteria combination 5 direction 12 1:4; 2:3; 3:2; 4:1 methods (Yager, 1988). 6 slope 18 1:8; 2:7; 3:6; 4:5; 5:4; This method consists of two categories of weights: 6:3; 7:2; 8:1 weights belongs to relative importance of criteria and sequential weights. Rinner and Malczewski have devel- Jankowski, Andrienko, and Andrienko (2001) used oped the ability of supporting the decision-making by the multi-criteria DECADE/Common GIS analysis on adding an OWA module to Common GIS. Thou, the decision-wing search and discovery stressed. studies have indicated that conventional OWA methods It is notable that, many studies have been done on have limited impacts when they have broad evaluation multivariate statistics in multi-criteria evaluation meth- criteria. In these situations, the combination of criteria ods too (Andrienko, Andrienko, & Jankowski, 2003). in such a way that assumptions are decisive will be very Providing location mapping and evaluating various complicated. areas is one of the most useful GIS applications for spatial For many criteria, the person encounters the issue of planning and management (Collins, Steiner, & Rushman, combining the criteria maps to the extent that their out- 2001). During the last decade, the issue of locating suit- comes meet the priorities. In such cases, the key aspects able applications has been used in multi-criteria evalua- of the decision issue may be indicated in terms of some tion methods in GIS increasingly, (Barredo, Benavides, fuzzy conceptual quantities such as “Most criteria must Hervás, & van Westen, 2000; Dai, Lee, & Zhang, 2001; be estimated” or “80% of the criteria must be met.” This Joerin, Thériault, & Musy, 2001). The conventional needs a change in the multi-criteria decision-making methods of multi-criteria analysis in GIS, like boolean methods, thus the conditions of these fuzzy quantities overlapping operators and weighted linear composition can be met (Boroushaki & Malczewski, 2008). GEOLOGY, ECOLOGY, AND LANDSCAPES 3 Figure 2. Height classes map. Figure 3. land use map. its area is over 200 km . The area is between 55 and 51 2. Material and methods and latitude 43 and 35. More precisely, the northern First of all, the natural features of the Roudehen area had boundary is formed by the mountain ridge of the north been studied. This study consist of topographic, slope, cli - of Lavasan to the hill of Emamzadeh Hashem. From the matic characteristics, soil talent, land use and orientation south, the mountains of Quch mountain, Suri Qal’eh and the area of Roudehen in terms of agricultural land by and … have separated this region from the central considering the OWA method has been studied in GIS. region of Iran. It has mild climate. Roudehen section Case Study of Area: Roudehen district, Damavand is in north of Amol city in Mazandaran province, and city, Tehran province. from the east it has neighbourhood with central part of Damavand, and from the west it has neighbourhood 2.1. Introducing the Roudehen section with the central part of Tehran (with Boumehen and Pardis town) and from the south it has neighbourhood The Roudehen section is 35 km North-east of Tehran with the mountains and Varamin and Pakdasht deserts province and in the foothills of southern Alborz south- (Figure 1). ern wall. The average height of the town is 1850 m and 4 F. TAVANA MEHRABANI AND A. NOHEHGAR Figure 4. climate classes. Figure 5. sloping classes map. 2.2. Research methodology [0,1] = W for j = 1, 2, …, n with map layers and stand- IJ ard weights, the OWA combination operator to the cell 2.2.1. OWA location i is a set of sequential weights v  = v , v , …, v 0 1 2 n Multi-criteria evaluation methods in GIS usually con- assigns, since for each j = 1, 2, …, n we will have. The sist of a set of spatial assessment criteria in the form of OWA combination operator is defined as follows (Yager, maps and layers. But the problem that normally hap- 1988): pens in spatial decision-making is how to combine the � � criteria maps with a set of descriptive values (weights) � u v j j OWA = z beside decision-makers’ preferences. Spontaneous deci- ∑ i n ij u v j=1 j=1 j j sion-making must lead to the selection of one (or mul- tiple options are spatial options. Each of these options, where in that z ≥ z ≥ ⋯ ≥ z by sorting the i1 i2 in (i = 1, 2, …, m), is described with a standardized set of a , a ,… , a i1 i2 in values (a ). The multi-criteria evaluation question con- ij Descriptive values are achieved and u is the same sists of a priority set as a weight of the criteria too, weight criterion that is sorted due to the order of z . As ij you can see, two types of weight have been used in this a ∈ [0, 1] for j = 1, 2, … , n ij GEOLOGY, ECOLOGY, AND LANDSCAPES 5 Figure 6. s oil classes. Figure 7. slope classes map. method: critical weights and sequential weights. The in relation to evaluation criteria. For instance, the fol- weight of the critical indicates the relative importance of lowing criteria can be combined: “More criteria are met,” each evaluation criteria (layers and maps), but sequential “All criteria must be met,” “At least half of the criteria weights are allocated according to the location of the cells are met.” of the layers and trays. It means that all cells in the same Such kinds of methods are called multi-criteria eval- position in several criteria maps will have the same com- uation by fuzzy quantizers (Yager, 1988). This method bined weights. Thus, all cells share a common weight in a consists of three main steps: (1) determining the type of nap, but there are varieties in their sequential weight. The the Q quantizer; (2) producing a set of sequential weights equation may be the same as the “weighted linear compo- related to Q; and (3) calculating and evaluating the posi- sition” in which the standard weights have been changed. tion of each of the cells using the combinative function OWA combination with quantitative fuzzy concepts. of OWA (Boroushaki & Malczewski, 2010). With a set of criteria maps and a quantizing fuzzy Case Study Parameters: Slope, direction, elevation, concept, Q can be used to combine maps with a “phrase” climate, soil and land use (Tables 1 and 2). 6 F. TAVANA MEHRABANI AND A. NOHEHGAR Figure 8. Map of agricultural power classes based on the method of regular average weighing. Boroushaki, S., & Malczewski, J. (2010). Using the fuzzy 3. Results majority approach for GIS-based multicriteria group See Figures 2–8. decision-making. Computers & Geosciences, 36(3), 302–312. Calijuri, M.L., Marques, E.T., Lorentz, J.F., Azevedo, R.F., & Carvalho, C.A. (2004). Multi-criteria analysis for the 4. Conclusion identification of waste disposal areas. Geotechnical and Geological Engineering, 22(2), 299–312. In current research, we have used GIS for preparing, Collins, M.G., Steiner, F.R., & Rushman, M.J. (2001). Land- evaluating, classifying and overlapping layers. We have use suitability analysis in the United States: Historical used six parameters for evaluating and classifying the development and promising technological achievements. power of the land. Finally, the areas that indicate the Environmental Management, 28(5), 611–621. Dai, F.C., Lee, C.F., & Zhang, X.H. (2001). GIS-based geo- best potential for farming and other classes are iden- environmental evaluation for urban land-use planning: A tified. Totally, using this technique, we are able to case study. Engineering Geology, 61(4), 257–271. evaluate the potential of the land for a variety of uses, Joerin, F., Thériault, M., & Musy, A. (2001). Using GIS and but there are more precise techniques that we recom- outranking multicriteria analysis for land-use suitability mend, the multi-criteria decision-making techniques assessment. International Journal of Geographical Information Science, 15(2), 153–174. combined with fuzzy functions must be used in next Jankowski, P., Andrienko, N., & Andrienko, G. (2001). Map- researches. centred exploratory approach to multiple criteria spatial decision making. International Journal of Geographical Information Science, 15(2), 101–127. Disclosure statement Heywood, I., Oliver, J., & Tomlinson, S. (1995). Building No potential conflict of interest was reported by the authors. an exploratory multi-criteria modelling environment for spatial decision support. Innovations in GIS, 2, 127–136. Karamiyan, R., Onaq, M., & Payamati, K. (2008). Water References management of kouhdasht of lorestan using territorial Andrienko, G., Andrienko, N., & Jankowski, P. (2003). planning. Journal of Agricultural Science and Natural Building spatial decision support tools for individuals and Resources, 15(2), 183–193. groups. Journal of Decision Systems, 1(2), 193–208. Leopold, L.B. (1968). Hydrology for urban land planning: Barredo, J., Benavides, A., Hervás, J., & van Westen, C.J. A guidebook on the hydrologic effects of urban land use. (2000). Comparing heuristic landslide hazard assessment Washington, DC: US Geological Survey. techniques using GIS in the Tirajana basin, Gran Canaria Makropoulos, C.K., Butler, D., & Maksimovic, C. (2003). Island, Spain. International Journal of Applied Earth Fuzzy logic spatial decision support system for urban Observation and Geoinformation, 2(1), 9–23. water management. Journal of Water Resources Planning Beedasy, J., & Whyatt, D. (1999). Diverting the tourists: A and Management, 129(1), 69–77. spatial decision-support system for tourism planning on Murphy, B.W. (2004). A revised land and soil capability a developing island. International Journal of Applied Earth classic fi ation for New South Wales . 13t International Soil Observation and Geoinformation, 1(3–4), 163–174. Conservation Organization Conference-Brisbane. Boroushaki, S., & Malczewski, J. (2008). Implementing an Tavakkol, M.S. (1997). e Th necessity of environmental extension of the analytical hierarchy process using ordered assessment of land in physical development plans. Journal weighted averaging operators with fuzzy quantifiers in of Environmental studies, University of Tehran, 18(18), ArcGIS. Computers & Geosciences, 34(4), 399–410. 61–74. GEOLOGY, ECOLOGY, AND LANDSCAPES 7 Van Beek, E. (2009). Managing water under current climate Yager, R.R. (1988). On ordered weighted averaging variability. In F. Ludwig, P. Kabat, H. van Schaik, & M. aggregation operators in multicriteria decisionmaking. Micheal van der valk (Eds.), Climate Change Adaptation in IEEE Transactions on Systems, Man, and Cybernetics, the Water Sector (pp. 51–77). London: Earthscan. 18(1), 183–190.

Journal

Geology Ecology and Landscapes – Taylor & Francis

Published: Jan 2, 2018

Keywords: Environmental factors; locating; land power; prevention; OWA method

References

  • Building spatial decision support tools for individuals and groups
  • Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain
  • Diverting the tourists: A spatial decision-support system for tourism planning on a developing island
  • Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS
  • Using the fuzzy majority approach for GIS-based multicriteria group decision-making
  • Multi-criteria analysis for the identification of waste disposal areas
  • Land-use suitability analysis in the United States: Historical development and promising technological achievements
  • GIS-based geo-environmental evaluation for urban land-use planning: A case study
  • Using GIS and outranking multicriteria analysis for land-use suitability assessment
  • Map-centred exploratory approach to multiple criteria spatial decision making
  • Building an exploratory multi-criteria modelling environment for spatial decision support
  • Water management of kouhdasht of lorestan using territorial planning
  • Fuzzy logic spatial decision support system for urban water management
  • The necessity of environmental assessment of land in physical development plans
  • On ordered weighted averaging aggregation operators in multicriteria decisionmaking