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Predicting the Spatial Distribution of Geological Hazards in Southern Sichuan, China, Using Machine Learning and ArcGIS

Author

Listed:
  • Ruizhi Zhang

    (School of Architecture, Southwest Jiaotong University, Chengdu 611756, China)

  • Dayong Zhang

    (School of Architecture and Urban Planning, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Bo Shu

    (School of Design, Southwest Jiaotong University, Chengdu 611756, China)

  • Yang Chen

    (School of Design, Southwest Jiaotong University, Chengdu 611756, China)

Abstract

Geological hazards in Southern Sichuan have become increasingly frequent, posing severe risks to local communities and infrastructure. This study aims to predict the spatial distribution of potential geological hazards using machine learning models and ArcGIS-based spatial analysis. A dataset comprising 2700 known geological hazard locations in Yibin City was analyzed to extract key environmental and topographic features influencing hazard susceptibility. Several machine learning models were evaluated, including random forest, XGBoost, and CatBoost, with model optimization performed using the Sparrow Search Algorithm (SSA) to enhance prediction accuracy. This study produced high-resolution susceptibility maps identifying high-risk zones, revealing a distinct spatial pattern characterized by a concentration of hazards in mountainous areas such as Pingshan County, Junlian County, and Gong County, while plains exhibited a relatively lower risk. Among different hazard types, landslides were found to be the most prevalent. The results further indicate a strong spatial overlap between predicted high-risk zones and existing rural settlements, highlighting the challenges of hazard resilience in these areas. This research provides a refined methodological framework for integrating machine learning and geospatial analysis in hazard prediction. The findings offer valuable insights for rural land use planning and hazard mitigation strategies, emphasizing the necessity of adopting a “small aggregations and multi-point placement” approach to settlement planning in Southern Sichuan’s mountainous regions.

Suggested Citation

  • Ruizhi Zhang & Dayong Zhang & Bo Shu & Yang Chen, 2025. "Predicting the Spatial Distribution of Geological Hazards in Southern Sichuan, China, Using Machine Learning and ArcGIS," Land, MDPI, vol. 14(3), pages 1-23, March.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:3:p:577-:d:1608714
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    References listed on IDEAS

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    1. Junpeng Huang & Sixiang Ling & Xiyong Wu & Rui Deng, 2022. "GIS-Based Comparative Study of the Bayesian Network, Decision Table, Radial Basis Function Network and Stochastic Gradient Descent for the Spatial Prediction of Landslide Susceptibility," Land, MDPI, vol. 11(3), pages 1-25, March.
    2. Shaohan Zhang & Shucheng Tan & Lifeng Liu & Duanyu Ding & Yongqi Sun & Jun Li, 2023. "Slope Rock and Soil Mass Movement Geological Hazards Susceptibility Evaluation Using Information Quantity, Deterministic Coefficient, and Logistic Regression Models and Their Comparison at Xuanwei, Ch," Sustainability, MDPI, vol. 15(13), pages 1-19, July.
    3. Ruixia Ma & Yan Lyu & Tianbao Chen & Qian Zhang, 2023. "Preliminary Risk Assessment of Geological Disasters in Qinglong Gorge Scenic Area of Taihang Mountain with GIS Based on Analytic Hierarchy Process and Logistic Regression Model," Sustainability, MDPI, vol. 15(22), pages 1-19, November.
    4. Jinming Zhang & Jianxi Qian & Yuefeng Lu & Xueyuan Li & Zhenqi Song, 2024. "Study on Landslide Susceptibility Based on Multi-Model Coupling: A Case Study of Sichuan Province, China," Sustainability, MDPI, vol. 16(16), pages 1-22, August.
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