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Factors’ feature optimization and flood susceptibility mapping in hilly regions: an Artificial Intelligence approach

Author

Listed:
  • Guangxu Liu

    (Gannan Normal University)

  • Zhiwei Wan

    (Gannan Normal University)

  • Haipei Liu

    (Wuhan University)

  • Baolei Li

    (Gannan Normal University)

  • Lihong Meng

    (Gannan Normal University)

  • Zhen Hu

    (Gannan Normal University)

  • Yingmin Liu

    (Gannan Normal University)

Abstract

Floods are among the most destructive natural disasters, threatening lives, infrastructure, and ecosystems. This study investigates flood susceptibility in Longnan, a hilly county of Jiangxi Province, China, using artificial intelligence (AI). We selected ten geographic and climatic factors—such as topography and precipitation—and identified five key contributors to flood risk: land use/cover (LULC), sediment transport index (STI), stream power index (SPI), slope (SLOP), and precipitation (PRE). Six AI models were evaluated, including Gradient Boosting (GB), AdaBoost (ADA), Random Forest (RF), Extra Trees (ET), Multi-Layer Perceptron (MLP), and Support Vector Classification (SVC). GB performed best, with an AUC of 0.92 and overall accuracy of 0.94. The GB model identified 45,509 raster cells (1.13% of the study area) as highly flood-prone, mostly alone the Taojiang River and in low-lying regions. Chenglong, Longnan, and Dujiang towns were found at highest risk. These results demonstrate AI’s potential to improve our understanding of flood dynamics and support targeted mitigation in complex hilly environments.

Suggested Citation

  • Guangxu Liu & Zhiwei Wan & Haipei Liu & Baolei Li & Lihong Meng & Zhen Hu & Yingmin Liu, 2025. "Factors’ feature optimization and flood susceptibility mapping in hilly regions: an Artificial Intelligence approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(16), pages 18601-18620, September.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:16:d:10.1007_s11069-025-07530-7
    DOI: 10.1007/s11069-025-07530-7
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