Insights from Optimized Non-Landslide Sampling and SHAP Explainability for Landslide Susceptibility Prediction

The quality of sampling data critically influences landslide susceptibility prediction accuracy. Current studies commonly use a 1:1 ratio of landslide to non-landslide samples, failing to reflect natural geographical variability. This study develops a region-specific framework by integrating SHAP (S...

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Detalhes bibliográficos

Principais autores:	Mengyuan Li, Hongling Tian
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2025-01-01
coleção:	Applied Sciences
Assuntos:	landslide susceptibility mapping landslide susceptibility prediction machine learning non-landslide sampling strategy SHAP
Acesso em linha:	https://www.mdpi.com/2076-3417/15/3/1163
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