Evaluating Landslide Hazard in Western Sichuan: Integrating Rainfall and Geospatial Factors Using a Coupled Information Value–Geographic Logistic Regression Model

Haipeng Zhou, Chenglin Mu (), Bo Yang, Gang Huang and Jinpeng Hong
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Haipeng Zhou: College of Engineering, Sichuan Normal University, Chengdu 610101, China
Chenglin Mu: College of Engineering, Sichuan Normal University, Chengdu 610101, China
Bo Yang: Sichuan Huadi Construction Engineering Co., Ltd., Chengdu 610081, China
Gang Huang: Sichuan Huadi Construction Engineering Co., Ltd., Chengdu 610081, China
Jinpeng Hong: College of Engineering, Sichuan Normal University, Chengdu 610101, China

Sustainability, 2025, vol. 17, issue 4, 1-30

Abstract: The western Sichuan region, characterized by unique geological conditions and the pronounced influence of uneven rainfall patterns, is highly vulnerable to frequent geological hazards, particularly landslides. These events pose significant threats to both public safety and regional ecosystem stability. This study focuses on landslide disasters in Dechang County, Sichuan Province, and introduces a framework for assessing landslide susceptibility. The framework incorporates nine critical factors: slope, aspect, topographic relief, distance from faults, slope structure, lithology, proximity to roads, hydrological systems, and vegetation coverage. Using ArcGIS and integrating rainfall as a key factor, we applied an information value–geographic logistic regression coupled model (GWILR) to evaluate landslide susceptibility across the region. The results show landslide susceptibility in Dechang County is classified into four categories: high (14.02%), moderate (54.06%), low (34.98%), and very low (0.94%). Landslides are most concentrated along fault lines and river systems. The model’s AUC value of 0.926 outperforms the traditional information entropy–logistic regression (ILR) model (AUC = 0.867), demonstrating superior predictive accuracy. The GWILR model offers key advantages over traditional methods. Unlike ILR, it assigns region-specific regression coefficients, capturing spatial heterogeneity and nonlinearity more effectively. The inclusion of rainfall as a key factor further enhances model accuracy by reflecting both temporal and spatial variations in landslide occurrence. This approach provides a more localized and precise evaluation of landslide risk, making it highly applicable for regions with complex geological and climatic conditions. This study highlights the GWILR model’s effectiveness in landslide susceptibility assessment and provides a foundation for improving disaster risk management in Dechang County and similar regions.

Keywords: geological hazards; landslide susceptibility; information value; geographic logistic regression coupled model (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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