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A python system for regional landslide susceptibility assessment by integrating machine learning models and its

Zizheng Guo1,2,3, Fei Guo1,2, Yu Zhang4,5

  • 1Hubei Key Laboratory of Disaster Prevention and Mitigation (China Three Gorges University), Yichang, 443002, China.

Heliyon
|November 29, 2023
PubMed
Summary

This study introduces an automated Python system for landslide susceptibility assessment, simplifying data processing and integrating multiple machine learning models. The system efficiently evaluates landslide risk, aiding non-professionals in hazard assessment.

Keywords:
GISLandslide susceptibility assessmentLoess plateauMachine learning modelsPython

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Area of Science:

  • Geosciences
  • Environmental Science
  • Computer Science

Background:

  • Landslide susceptibility assessment is crucial for risk management but often involves complex, multi-software preprocessing.
  • Existing methods limit the integration of diverse machine learning models for susceptibility analysis.

Purpose of the Study:

  • To develop an automated Python system for regional landslide susceptibility assessment.
  • To integrate data processing, machine learning modeling, and result evaluation into a single, efficient workflow.
  • To provide accessible tools for landslide susceptibility analysis, particularly for non-experts.

Main Methods:

  • A Python system with three modules: geographic data processing, machine learning modeling, and result evaluation.
  • Utilized ten landslide influencing factors and the frequency ratio method for data preprocessing.
  • Integrated four machine learning models: logistic regression (LR), multi-layer perceptron (MLP), support vector machine (SVM), and extreme gradient boosting (XGBoost).
  • Employed receiver operating characteristic (ROC) curves for model accuracy evaluation.

Main Results:

  • The system was successfully applied to Lantian County, Shaanxi Province.
  • Achieved high accuracy with SVM (AUC=0.882), followed by MLP (AUC=0.812), XGBoost (AUC=0.809), and LR (AUC=0.838).
  • Identified SVM as the most suitable model for landslide susceptibility in the study area.

Conclusions:

  • The developed Python system enhances the efficiency of regional landslide susceptibility assessment.
  • The system offers valuable tools for data processing and modeling, benefiting both professionals and non-professionals.
  • Open-sourcing the system on GitHub promotes wider adoption and accessibility for landslide hazard analysis.