Geospatial SHAP interpretability for urban road collapse susceptibility assessment: a case study in Hangzhou, China
The issue of weak interpretability in geological disaster susceptibility assessments using machine learning models has been a long-standing concern. Although SHAP (Shapley Additive Explanations) models have been extensively used in recent years to interpret the decision-making details of models, the...
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| Главные авторы: | , , , , , , , |
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| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Taylor & Francis Group
2025-12-01
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| Серии: | Geomatics, Natural Hazards & Risk |
| Предметы: | |
| Online-ссылка: | https://www.tandfonline.com/doi/10.1080/19475705.2025.2491473 |
| Метки: |
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