Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection

Gully erosion susceptibility mapping is an essential land management tool to reduce soil damages. This study investigates gully based on multiple diagnostic analysis, support vector machine and random forest algorithms, also a combination of these models, namely the ensemble model. Thus, map in Kondoran watershed Iran was generated by applying models occurrence non-occurrence points (as target variable) several predictors (slope, aspect, elevation, topographic wetness index, drainage density, plan curvature, distance streams, lithology, texture use). The Boruta algorithm used select most effective variables modeling susceptibility. area under receiver operating characteristic curve (AUC), characteristics, true skill statistics (TSS) were assess model performance. results indicated that had best performance (AUC = 0.982, TSS 0.93) compared others. factors region topological, anthropogenic, geological. methodology this can be other regions control mitigate phenomenon biophilic regenerative techniques at locations influential factors.