Gully Erosion Susceptibility Mapping in Highly Complex Terrain Using Machine Learning Models
Bibliographic Data
| ID | 22034020 |
|---|---|
| Authors | Annan Yang (0000-0001-7566-2904, Northwest University), Chunmei Wang (0000-0001-9521-0454, Northwest University), Guowei Pang (0000-0002-4571-466X, Northwest University), Yongqing Long (0000-0001-7218-4353, Northwest University, corresponding author), Lei Wang (0000-0001-5326-565X, Northwest University), Richard M Cruse (0000-0002-8995-3064, Iowa State University), Qinke Yang (0000-0003-3237-6558, Northwest University) |
| Year | 2021 |
| Volume | 10 |
| Issue | 10 |
| Pages | 680 |
| Publication date | 2021-10-09 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10100680 |
| OpenAlex | W3205782293 |
| Language | EN |
| Citations received | 1 |
| References cited | 65 |
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
Algorithm · Artificial neural network · Cartography · Decision tree · Erosion · Geography · Geomorphology · Gradient boosting · Gully Erosion · Interpretability · Machine learning · Overfitting · Random forest · Terrain · Computer Science · Flood Risk Assessment and Management · Groundwater and Watershed Analysis · Mathematics · Soil erosion and sediment transport · Artificial Intelligence · Geology
| Unique citing works | 1 |
|---|---|
| Citations per year | 0.25 |
| Citation span | 2022 - 2022 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |