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This is a Quantitative Study study.

September 16, 2025Open Access

Geohazard Susceptibility Assessment in Karst Terrain: A Novel Coupling Model Integrating Information Value and XGBoost Machine Learning in Guizhou Province, China

CJChen JiaoZhejiang University FWFufei WuGuizhou Normal University HHHongyin HuJiangnan University

Key Points

The IV-XGBoost model achieved the best performance with an AUC of 0.9448 in geohazard susceptibility assessment.
High-risk areas identified include northern Wujiang River Basin and the central karst core area, indicating targeted disaster prevention needs.
The study utilized various machine learning algorithms and the Bayesian optimization approach for precise modeling.
The research underscores the significance of factors like NDVI, land use type, and elevation in assessing geological disaster susceptibility.

Abstract

In this study, the geological disasters in Guizhou Province serve as the research object, and a systematic susceptibility evaluation is conducted in light of the province’s prominent problems with frequent geological disasters. The current research primarily focuses on the application of a single model, often with deficiencies in factor interpretation. It has not yet systematically integrated the advantages of the traditional information model and multiple machine learning algorithms, nor introduced interpretable methods to analyze the disaster mechanism deeply. In this study, the information value (IV) model is combined with machine learning algorithms—logistic regression (LR), decision tree (DT), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost)—to construct a coupling model to evaluate the susceptibility to geological disasters. Combined with the Bayesian optimization algorithm, the geological disaster susceptibility evaluation model is built. The confusion matrix and receiver operating characteristic (ROC) curve were used to evaluate the model’s accuracy. The Shapley Additive exPlanations (SHAP) method is used to quantify the contribution of each influencing factor, thereby improving the transparency and credibility of the model. The results show that the coupling models, especially the IV-XGB model, achieved the best performance (AUC = 0.9448), which significantly identifies the northern Wujiang River Basin and the central karst core area as high-risk areas and clarifies the disaster-causing mechanism of “terrain–hydrology–human activities” coupling. The SHAP method further identified that NDVI, land use type, and elevation were the predominant controlling factors. This study presents a high-precision and interpretable modeling method for assessing susceptibility to geological disasters, providing a scientific basis for disaster prevention and control in Guizhou Province and similar geological conditions.

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