Rainfall-Induced Geological Hazard Susceptibility Assessment in the Henan Section of the Yellow River Basin: Multi-Model Approaches Supporting Disaster Mitigation and Sustainable Development

Yinyuan Zhang, Hui Ci (), Hui Yang, Ran Wang and Zhaojin Yan
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Yinyuan Zhang: School of Resources and Earth Sciences, China University of Mining and Technology, Xuzhou 221116, China
Hui Ci: School of Resources and Earth Sciences, China University of Mining and Technology, Xuzhou 221116, China
Hui Yang: School of Resources and Earth Sciences, China University of Mining and Technology, Xuzhou 221116, China
Ran Wang: School of Resources and Earth Sciences, China University of Mining and Technology, Xuzhou 221116, China
Zhaojin Yan: School of Resources and Earth Sciences, China University of Mining and Technology, Xuzhou 221116, China

Sustainability, 2025, vol. 17, issue 10, 1-22

Abstract: The Henan section of the Yellow River Basin (3.62 × 10 4 km 2 , 21.7% of Henan Province), a vital agro-industrial and politico-economic hub, faces frequent rainfall-induced geohazards. The 2021 “7·20” Zhengzhou disaster, causing 398 fatalities and CNY 120.06 billion loss, highlights its vulnerability to extreme weather. While machine learning (ML) aids geohazard assessment, rainfall-induced geological hazard susceptibility assessment (RGHSA) remains understudied, with single ML models lacking interpretability and precision for complex disaster data. This study presents a hybrid framework (IVM-ML) that integrates the Information Value Model (IVM) and ML. The framework uses historical disaster data and 11 factors (e.g., rainfall erosivity, relief amplitude) to calculate information values and construct a machine learning prediction model with these quantitative results. By combining IVM’s spatial analysis with ML’s predictive power, it addresses the limitations of conventional single models. ROC curve validation shows the Random Forest (RF) model in IVM-ML achieves the highest accuracy (AUC = 0.9599), outperforming standalone IVM (AUC = 0.7624). All models exhibit AUC values exceeding 0.75, demonstrating strong capability in capturing rainfall–hazard relationships and reliable predictive performance. Findings support RGHSA practices in the mid-Yellow River urban cluster, offering insights for sustainable risk management, land-use planning, and climate resilience. Bridging geoscience and data-driven methods, this study advances global sustainability goals for disaster reduction and environmental security in vulnerable riverine regions.

Keywords: rainfall-induced geological hazard; information value model; machine learning; multi-model fusion; susceptibility assessment; sustainable development goals (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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