Assessment of gully erosion susceptibility using four data-driven models AHP, FR, RF and XGBoosting machine learning algorithms
Gully erosion is a significant global threat to socioeconomic and environmental sustainability, making it a widespread natural hazard. Developing spatial models for gully erosion is crucial for local governance to effectively implement mitigation measures and promote regional development. This study...
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| Huvudupphov: | , |
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| Materialtyp: | Artigo |
| Språk: | Inglês |
| Utgiven: |
KeAi Communications Co. Ltd.
2025-03-01
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| Serie: | Natural Hazards Research |
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| Länkar: | http://www.sciencedirect.com/science/article/pii/S2666592124000362 |
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