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A Geoinformatics Approach to Water ErosionAssessments of Erosion Risk

A Geoinformatics Approach to Water Erosion: Assessments of Erosion Risk [This chapter presents methods for identifying and classifying areas subject to soil degradationSoil degradation risks—such as soil lossSoil loss, water ponding, and sediment depositionDeposition. It begins in the first section with the exploration of the application of the topographic thresholdTopographic threshold in mapping areas that are, or are not, at risk of soil incision. Then, the second section describes an expert-based system for identifying areas at risk, based on weighting the contributing variables as defined by experts. The third section of the chapter describes data-miningData mining techniques for studying soil degradationSoil degradation risks, using machine-learning methodology and training sets. Finally, in its fourth and final section, a fuzzy rule-based system is used to simulate soil lossSoil loss and depositionDeposition in a spatially and temporally explicit manner, based on subjective probability, as formulated through fuzzy mathematics. Fuzzy rule-based modeling makes it possible to bypass more rigorous computations of exact physically based models. The methods demonstrated in this chapter are simple and easy-to-use—and can be applied to map soil degradationSoil degradation risks using the geoinformatics data stored in GIS layers.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Geoinformatics Approach to Water ErosionAssessments of Erosion Risk

Svoray, Tal
Springer Journals — Feb 17, 2022
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Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2022
ISBN
978-3-030-91535-3
Pages
205 –263
DOI
10.1007/978-3-030-91536-0_6
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter presents methods for identifying and classifying areas subject to soil degradationSoil degradation risks—such as soil lossSoil loss, water ponding, and sediment depositionDeposition. It begins in the first section with the exploration of the application of the topographic thresholdTopographic threshold in mapping areas that are, or are not, at risk of soil incision. Then, the second section describes an expert-based system for identifying areas at risk, based on weighting the contributing variables as defined by experts. The third section of the chapter describes data-miningData mining techniques for studying soil degradationSoil degradation risks, using machine-learning methodology and training sets. Finally, in its fourth and final section, a fuzzy rule-based system is used to simulate soil lossSoil loss and depositionDeposition in a spatially and temporally explicit manner, based on subjective probability, as formulated through fuzzy mathematics. Fuzzy rule-based modeling makes it possible to bypass more rigorous computations of exact physically based models. The methods demonstrated in this chapter are simple and easy-to-use—and can be applied to map soil degradationSoil degradation risks using the geoinformatics data stored in GIS layers.]

Published: Feb 17, 2022

Keywords: AHP; Expert systems; Data mining; Fuzzy logic; Machine learning; Topographic threshold

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