Abstract
Gully erosion susceptibility mapping (GESM) is a valuable tool for sustainable land use management and reducing soil erosion. Gully erosion and its formation are a natural process; it greatly threatens agriculture, environment, ecosystem disruption, and natural resources. The objective of this present study is to develop a GESM by implementation of well acceptable SVM learning algorithm in Golestan Province, Kalaleh Township, Iran. Primarily, gully sites were obtained by comprehensive field observations. After that, 12 gully erosion predisposing factors were selected to assess the gully erosion susceptibility map. The 12 conditioning factors were aspect, altitude, drainage density, lithology, slope angle, slope length, distance from river, profile curvature, drainage density, TWI, distance from road, and plan curvature. Finally, gully erosion susceptibility map was prepared using the SVM model in “R” environment. In the final stage, assessment of the prediction accuracy of the susceptibility model with the help of training (70%) and validation datasets (30%) of gully location was done. The predicted susceptibility map was validated with the help of receiver operating characteristic (ROC) curve, true skill statistics (TSS), and deviance value. The results indicated that the areas under the curve (AUC) were calculated as 94.3% and 97.0% based on validation and training dataset, respectively. Furthermore, the TSS, deviance, and correlation values were 0.84, 0.50, and 0.85, respectively. So, the results of other indices including, sensitivity, specificity, and Cohen’s Kappa (CK) showed that SVM model has reasonable prediction accuracy for the cases of gully erosion susceptibility assessment. As regards the SVM model, a total area of 11.66% was identified as the hazard prone area of the mentioned Town ship. So, it is concluded that the gully erosion map serves as an important tool for protective action and watershed management, specifically at the initiation of the gully to protect the development of land degradation.
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Pourghasemi, H.R., Gayen, A., Haque, S.M., Bai, S. (2020). Gully Erosion Susceptibility Assessment Through the SVM Machine Learning Algorithm (SVM-MLA). In: Shit, P., Pourghasemi, H., Bhunia, G. (eds) Gully Erosion Studies from India and Surrounding Regions. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-23243-6_28
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