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 specialized skills required and the non-intuitiveness of SHAP plots make their application challenging in practical decision-making environments. In response, our study introduces a map-based SHAP visualization framework to enhance the interpretability of susceptibility assessment results. Utilizing Optuna for hyperparameter tuning, we developed a high-performance XGBoost model to assess the susceptibility of the most impactful disaster in Hangzhou: urban road collapses. In addition to interpreting the contributions of evaluation factors through traditional SHAP summaries and bar plots, we displayed the SHAP values for each evaluation factor using map visualizations, and discussed the model’s sensitivity to different values. To validate the alignment between model predictions and physical collapse mechanisms, our study selected typical collapse cases, interpreted these cases combining map visualizations, SHAP force plots at collapse points, and the physical mechanisms of collapse. Our research improves the interpretability of susceptibility assessments with machine learning by using map visualizations, providing new insights into spatial effects and robust support for urban decision-making applications.