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Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning

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  • Hailang He

    (School of Safety Science and Engineering, Tsinghua University, Beijing 100084, China
    Hefei Institute for Public Safety Research, Tsinghua University, Hefei 230601, China
    Defense Engineering Institute, AMS, PLA, Beijing 100036, China)

  • Weiwei Wang

    (Hefei Institute for Public Safety Research, Tsinghua University, Hefei 230601, China
    Primus Line (China) Ltd., Shanghai 201108, China)

  • Zhengxing Wang

    (Hefei Institute for Public Safety Research, Tsinghua University, Hefei 230601, China)

  • Shu Li

    (Hefei Institute for Public Safety Research, Tsinghua University, Hefei 230601, China
    Anhui Key Laboratory of Bridge and Tunnel Engineering Testing, Hefei 230051, China)

  • Jianguo Chen

    (School of Safety Science and Engineering, Tsinghua University, Beijing 100084, China)

Abstract

The accuracy of Seismic Landslide Susceptibility Maps (SLSMs) is imperative for the prevention of seismic landslide disasters. This study enhances the precision of SLSMs by integrating nine distinct machine learning methodologies with the GeoDetector version 0.0.4 tool to filter both numerical and physical factors contributing to landslide susceptibility. The dataset comprises 2317 landslide instances triggered by the 2013 Minxian Ms = 6.6 earthquake, from which redundant factors were pruned using the Recursive Feature Elimination technique. Subsequent evaluations of the optimized factors, both individually and in combination, were conducted through Frequency Ratio analysis and Factor Interaction assessment. The study juxtaposes the Area Under the Receiver Operating Characteristic Curve (AUC) and the accuracy of nine machine learning models before and after factor optimization. The findings indicate an increase in AUC from a maximum of 0.989 to 0.992 in the Random Forest model, and an 8.37% increase in AUC for the SVM model, signifying a notable enhancement in the stability across all models. The establishment of the SLSM notably elevated the frequency ratio in high-risk zones from 50.40 to 85.14, underscoring the efficacy of combining machine learning and detector optimization techniques in sustainable practices. This research proposes a universal framework aimed at eliminating redundancy and noise in SLSMs and hazard risk assessments, thereby facilitating sustainable geological disaster risk management.

Suggested Citation

  • Hailang He & Weiwei Wang & Zhengxing Wang & Shu Li & Jianguo Chen, 2024. "Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning," Sustainability, MDPI, vol. 16(9), pages 1-24, May.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3828-:d:1387822
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