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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2025-01-01
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| author | Mengyuan Li Hongling Tian |
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| description | 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 (SHapley Additive exPlanation) analysis with twelve landslide conditioning factors (LCFs) and three progressive sampling strategies, aiming to create adaptive non-landslide point selection criteria tailored to unique environmental and geological characteristics. The strategies include (1) multi-ratio random sampling (1:1 to 1:200), (2) susceptibility-based sampling adjustments derived from pre-susceptibility analysis, and (3) LCF-based correction using the NDVI threshold identified through SHAP analysis. Results show that LCF-based correction achieved the highest performance, while a 1:5 ratio proved optimal in random sampling, aligning with regional characteristics. This framework demonstrates the importance of region-specific sampling strategies in improving landslide susceptibility prediction. |
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| institution | DOAJ |
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| publishDate | 2025-01-01 |
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| spelling | doaj-art-5e2af113cfdf49bbbb29bbcc24ec28342025-08-20T02:48:01ZengMDPI AGApplied Sciences2076-34172025-01-01153116310.3390/app15031163Insights from Optimized Non-Landslide Sampling and SHAP Explainability for Landslide Susceptibility PredictionMengyuan Li0Hongling Tian1State Key Laboratory of Mountain Hazards and Engineering Resilience, Chengdu 610299, ChinaState Key Laboratory of Mountain Hazards and Engineering Resilience, Chengdu 610299, ChinaThe 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 (SHapley Additive exPlanation) analysis with twelve landslide conditioning factors (LCFs) and three progressive sampling strategies, aiming to create adaptive non-landslide point selection criteria tailored to unique environmental and geological characteristics. The strategies include (1) multi-ratio random sampling (1:1 to 1:200), (2) susceptibility-based sampling adjustments derived from pre-susceptibility analysis, and (3) LCF-based correction using the NDVI threshold identified through SHAP analysis. Results show that LCF-based correction achieved the highest performance, while a 1:5 ratio proved optimal in random sampling, aligning with regional characteristics. This framework demonstrates the importance of region-specific sampling strategies in improving landslide susceptibility prediction.https://www.mdpi.com/2076-3417/15/3/1163landslide susceptibility mappinglandslide susceptibility predictionmachine learningnon-landslide sampling strategySHAP |
| spellingShingle | Mengyuan Li Hongling Tian Insights from Optimized Non-Landslide Sampling and SHAP Explainability for Landslide Susceptibility Prediction Applied Sciences landslide susceptibility mapping landslide susceptibility prediction machine learning non-landslide sampling strategy SHAP |
| title | Insights from Optimized Non-Landslide Sampling and SHAP Explainability for Landslide Susceptibility Prediction |
| title_full | Insights from Optimized Non-Landslide Sampling and SHAP Explainability for Landslide Susceptibility Prediction |
| title_fullStr | Insights from Optimized Non-Landslide Sampling and SHAP Explainability for Landslide Susceptibility Prediction |
| title_full_unstemmed | Insights from Optimized Non-Landslide Sampling and SHAP Explainability for Landslide Susceptibility Prediction |
| title_short | Insights from Optimized Non-Landslide Sampling and SHAP Explainability for Landslide Susceptibility Prediction |
| title_sort | insights from optimized non landslide sampling and shap explainability for landslide susceptibility prediction |
| topic | landslide susceptibility mapping landslide susceptibility prediction machine learning non-landslide sampling strategy SHAP |
| url | https://www.mdpi.com/2076-3417/15/3/1163 |
| work_keys_str_mv | AT mengyuanli insightsfromoptimizednonlandslidesamplingandshapexplainabilityforlandslidesusceptibilityprediction AT honglingtian insightsfromoptimizednonlandslidesamplingandshapexplainabilityforlandslidesusceptibilityprediction |