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Landslide susceptibility prediction improvements based on a semi-integrated supervised machine learning model.

Ning Yang1,2, Rui Wang3,4, Zhaofei Liu1,2

  • 1Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.

Environmental Science and Pollution Research International
|February 15, 2023
PubMed
Summary

This study introduces a semi-integrated supervised approach to enhance machine learning (ML) models for landslide susceptibility mapping. The new method improves prediction accuracy by addressing issues with sample selection and model application in landslide studies.

Keywords:
Integrated modelLandslide susceptibility studyMachine learningNon-landslide sampleSemi-integrated supervisionTrue skill statistic

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Area of Science:

  • Geosciences
  • Environmental Science
  • Machine Learning Applications

Background:

  • Traditional landslide susceptibility studies face challenges with model effectiveness, insufficient disaster samples, and poor non-hazard sample selection.
  • Machine learning (ML) models are widely used but can suffer from these data and application limitations.

Purpose of the Study:

  • To propose and evaluate a semi-integrated supervised approach to improve ML model performance in landslide susceptibility assessment.
  • To address common problems in landslide susceptibility studies, including data limitations and model variability.

Main Methods:

  • A semi-integrated supervised method was developed using K-nearest neighbors (KNN), random forest (RF), and Bayesian-regularized neural network (BRNN) models.
  • New landslide and non-landslide samples were identified and integrated with original data to train ensemble-supervised models (ESKNN, ESRF, ESBRNN).
  • Model accuracy was evaluated using Area Under the Curve (AUC), True Skill Statistic (TSS), and Frequency Ratio (FR).

Main Results:

  • The semi-integrated supervised method significantly improved the accuracy of traditional ML models.
  • The ensemble-supervised random forest (ESRF) model demonstrated the best prediction performance with AUC = 0.939, TSS = 0.440, and FR = 95.8%.
  • The proposed approach effectively addressed issues related to model application, data scarcity, and non-landslide sample selection.

Conclusions:

  • The semi-integrated supervised ML approach offers a robust solution for enhancing landslide susceptibility mapping.
  • This method provides valuable insights for improving landslide data acquisition and model generalization in geoscientific studies.
  • The findings contribute to more reliable landslide hazard assessments and risk management strategies.