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

Hamed Ahmadpour, Ommolbanin Bazrafshan, Elham Rafiei-Sardooi, Hossein Zamani and Thomas Panagopoulos
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Hamed Ahmadpour: Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources Engineering, University of Hormozgan, Bandar Abbas 7916193145, Iran
Ommolbanin Bazrafshan: Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources Engineering, University of Hormozgan, Bandar Abbas 7916193145, Iran
Elham Rafiei-Sardooi: Department of Ecological Engineering, Faculty of Natural Resources, University of Jiroft, Kerman 7867161167, Iran
Hossein Zamani: Department of Mathematics and Statistics, Faculty of Science, University of Hormozgan, Bandar Abbas 7916193145, Iran
Thomas Panagopoulos: Research Center for Spatial and Organizational Dynamics, University of Algarve, Gambelas Campus, 8005 Faro, Portugal

Sustainability, 2021, vol. 13, issue 18, 1-24

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

Keywords: ensemble modeling; data mining; gully erosion; watershed management; land use (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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