Paper Info
Title
Gully Erosion Susceptibility Mapping in Highly Complex Terrain Using Machine Learning Models
Abstract
Gully erosion is the most severe type of water erosion and is a major land degradation process. Gully erosion susceptibility mapping (GESM)'s efficiency and interpretability remains a challenge, especially in complex terrain areas. In this study, a WoE-MLC model was used to solve the above problem, which combines machine learning classification algorithms and the statistical weight of evidence (WoE) model in the Loess Plateau. The three machine learning (ML) algorithms utilized in this research were random forest (RF), gradient boosted decision trees (GBDT), and extreme gradient boosting (XGBoost). The results showed that: (1) GESM were well predicted by combining both machine learning regression models 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 GESM than the other two algorithms, with the strongest generalization ability and best performance in avoiding overfitting (averaged AUC = 0.947), followed by the RF algorithm (averaged AUC = 0.944), and GBDT algorithm (averaged AUC = 0.938); and (3) slope gradient, land use, and altitude were the main factors for GESM. This study may provide a possible method for gully erosion susceptibility mapping at large scale.</p>
Year
DOI
Venue
2021
10.3390/ijgi10100680
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
Keywords
DocType
Volume
gully erosion, machine learning, the weight of evidence, gully erosion susceptibility mapping, Loess Plateau
Journal
10
Issue
Citations 
PageRank 
10
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Annan Yang100.68
Chunmei Wang200.34
Guowei Pang300.34
Yongqing Long400.68
Lei Wang500.34
Richard M. Cruse600.34
Qinke Yang700.34