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Risk Assessment and Spatial Zoning of Rainstorm and Flood Hazards in Mountainous Cities Using the Random Forest Algorithm and the SCS Model

Author

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  • Zixin Xie

    (School of Architecture and Civil Engineering, Xihua University, Chengdu 611756, China)

  • Bo Shu

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

Abstract

China has a vast land area, with mountains accounting for 1/3 of the country’s land area. Flooding in these areas can cause significant damage to human life and property. Therefore, rainstorms and flood hazards in Huangshan City should be accurately assessed and effectively managed to improve urban resilience, promote green and low-carbon development, and ensure socio-economic stability. Through the Random Forest (RF) algorithm and the Soil Conservation Service (SCS) model, this study aimed to assess and demarcate rainstorm and flood hazard risks in Huangshan City. Specifically, Driving forces-Pressure-State-Impact-Response (DPSIR)’s framework was applied to examine the main influencing factors. Subsequently, the RF algorithm was employed to select 11 major indicators and establish a comprehensive risk assessment model integrating four factors: hazard, exposure, vulnerability, and adaptive capacity. Additionally, a flood hazard risk zoning map of Huangshan City was generated by combining the SCS model with a Geographic Information System (GIS)-based spatial analysis. The assessment results reveal significant spatial heterogeneity in rainstorm and flood risks, with higher risks concentrated in low-lying areas and urban fringes. In addition, precipitation during the flood season and economic losses were identified as key contributors to flood risk. Furthermore, flood risks in certain areas have intensified with ongoing urbanization. The evaluation model was validated by the 7 July 2020 flood event, suggesting that Huangshan District, Huizhou District, and northern Shexian County suffered the most severe economic losses. This confirms the reliability of the model. Finally, targeted flood disaster prevention and mitigation strategies were proposed for Huangshan City, particularly in the context of carbon neutrality and green urbanization, providing decision-making support for disaster prevention and emergency management. These recommendations will contribute to enhancing the city’s disaster resilience and promoting sustainable urban development.

Suggested Citation

  • Zixin Xie & Bo Shu, 2025. "Risk Assessment and Spatial Zoning of Rainstorm and Flood Hazards in Mountainous Cities Using the Random Forest Algorithm and the SCS Model," Land, MDPI, vol. 14(3), pages 1-25, February.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:3:p:453-:d:1597208
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    References listed on IDEAS

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    3. Junfei Chen & Qian Li & Huimin Wang & Menghua Deng, 2019. "A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta, China," IJERPH, MDPI, vol. 17(1), pages 1-21, December.
    4. Yu Qiu & Yuan Liu & Yang Liu & Yingzi Chen & Yu Li, 2019. "An Interval Two-Stage Stochastic Programming Model for Flood Resources Allocation under Ecological Benefits as a Constraint Combined with Ecological Compensation Concept," IJERPH, MDPI, vol. 16(6), pages 1-18, March.
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