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Land degradation caused by soil erosion remains an important global issue due to its adverse consequences on food security and environment. Geospatial prediction of erosion through susceptibility analysis is very crucial to sustainable watershed management. Previous susceptibility studies devoid of some crucial conditioning factors (CFs) termed dynamic CFs whose impacts on the accuracy have not been investigated. Thus, this study evaluates erosion susceptibility under the influence of both non-redundant static and dynamic CFs using support vector machine (SVM), remote sensing and GIS. The CFs considered include drainage density, lineament density, length-slope and soil erodibility as non-redundant static factors, and land surface temperature, soil moisture index, vegetation index and rainfall erosivity as the dynamic factors. The study implements four kernel tricks of SVM with sequential minimal optimization algorithm as a classifier for soil erosion susceptibility modeling. Using area under the curve (AUC) and Cohen’s kappa index (k) as the validation criteria, the results showed that polynomial function had the highest performance followed by linear and radial basis function. However, sigmoid SVM underperformed having the lowest AUC and k values coupled with higher classification errors. The CFs’ weights were implemented for the development of soil erosion susceptibility map. The map would assist planners and decision makers in optimal land-use planning, prevention of soil erosion and its related hazards leading to sustainable watershed management.

SVM-Based Geospatial Prediction of Soil Erosion Under Static and Dynamic Conditioning Factors