An ensemble model for gully erosion susceptibility mapping in highly complex terrain area

Gully erosion is the most severe type of water erosion and is a major land degradation process. Predicting gully erosion susceptibility (GES) map efficiently and interpretably remains a challenge, especially in complex terrain areas. In this study, a new method called WoE-MLC model was used to solve the above problem, which combined machine learning classification algorithms and the weight of evidence (WoE) model in the Loess Plateau. The three machine learning algorithms taken into account included random forest (RF), gradient boosted decision trees (GBDT), and extreme gradient boosting (XGBoost). And the performance of the models was evaluated by the receiver operating characteristic (ROC) curve. The results showed that: (1) GES maps were well predicted by machine learning regression and WoE-MLC models, with the area under the curve (AUC) values both greater than 0.92, and the latter was more computationally efficient and interpretable; (2) The XGBoost algorithm was more efficient in GES map than the other two algorithms, with the stronger generalization ability and best performance in avoiding overfitting (averaged AUC = 0.947), followed by the RF (averaged AUC = 0.944), and GBDT algorithm (averaged AUC = 0.938); (3) Slope gradient, land use, and altitude were the main factors for GES mapping. This study may provide a possible method for gully erosion susceptibility mapping at large scale.