Abstract
In the present study, gully erosion susceptibility was evaluated for the area of the Robat Turk Watershed in Iran. The assessment of gully erosion susceptibility was performed using four state-of-the-art data mining techniques: random forest (RF), credal decision trees (CDTree), kernel logistic regression (KLR), and best-first decision tree (BFTree). To the best of our knowledge, the KLR and CDTree algorithms have been rarely applied to gully erosion modeling. In the first step, from the 242 gully erosion locations that were identified, 70% (170 gullies) were selected as the training dataset, and the other 30% (72 gullies) were considered for the result validation process. In the next step, twelve gully erosion conditioning factors, including topographic, geomorphological, environmental, and hydrologic factors, were selected to estimate gully erosion susceptibility. The area under the ROC curve (AUC) was used to estimate the performance of the models. The results revealed that the RF model had the best performance (AUC = 0.893), followed by the KLR (AUC = 0.825), the CDTree (AUC = 0.808), and the BFTree (AUC = 0.789) models. Overall, the RF model performed significantly better than the others, which may support the application of this method to a transferable susceptibility model in other areas. Therefore, we suggest using the RF, KLR, and CDT models for gully erosion susceptibility mapping in other prone areas to assess their reproducibility.
Highlights
Given the harmful effects of gully erosion, strategies for managing and reducing the damage caused by this phenomenon are essential to achieve sustainable development [1]
The findings indicated that the Logistic Model Tree (LMT) model provided better performance than that of the Alternating Decision Tree (ADTree) and Naïve-Bayes tree (NBTree) models
The performance of these models will be evaluated through the following statistical measures: sensitivity, specificity, and accuracy
Summary
Given the harmful effects of gully erosion, strategies for managing and reducing the damage caused by this phenomenon are essential to achieve sustainable development [1]. One of the strategies to achieve this goal is the use of gully erosion susceptibility mapping (GESM) [2]. This is well-known as an essential technique to address the mechanisms of gully erosion. KLR is a powerful classification technique compared to other traditional classification methods [66] This model has been successfully used in many of the classified problems [86]. When the optimization algorithm is suitable, as the algorithm does not need to solve the quadratic equation, it can perform analysis more quickly than other algorithms such as SVM [45,92] To apply this model, the statistical software R (version 3.5.2) was used
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