EGU22-1461, updated on 27 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Annan Yang1,2, Chunmei Wang1,2, Guowei Pang1,2, Yongqing Long1,2, Lei Wang1,2, Richard M. Cruse3, and Qinke Yang1,2
Annan Yang et al.
  • 1Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity , College of Urban and Environmental Sciences, Northwest University , Xi’an 710127, China
  • 2Key Laboratory of National Forestry Administration on Ecological Hydrology and Disaster Prevention in Arid Regions, Northwest University , Xi’an 710127, China
  • 3Department of Agronomy , Iowa State University , Ames, IA 50011, USA

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.

How to cite: Yang, A., Wang, C., Pang, G., Long, Y., Wang, L., M. Cruse, R., and Yang, Q.: An ensemble model for gully erosion susceptibility mapping in highly complex terrain area, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1461,, 2022.