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Game-theoretic optimization of landslide susceptibility mapping: a comparative study between Bayesian-optimized basic

Javed Mallick1, Meshel Alkahtani2, Hoang Thi Hang3

  • 1Department of Civil Engineering, College of Engineering, King Khalid University, P.O. Box: 394, Abha, 61411, Kingdom of Saudi Arabia. jmallick@kku.edu.sa.

Environmental Science and Pollution Research International
|April 9, 2024
PubMed
Summary

This study optimizes deep neural networks (DNN), Elman neural networks (ENN), and artificial neural networks (ANN) for landslide susceptibility mapping. The deep neural network model demonstrated superior performance in identifying high-risk landslide zones.

Keywords:
Bayesian optimizationLandslide risk mitigationLandslide susceptibilityNeural networkSHapley Additive exPlanations (SHAP)

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Area of Science:

  • Geosciences
  • Artificial Intelligence
  • Environmental Science

Background:

  • Landslide susceptibility mapping (LSM) is crucial for mitigating risks to lives and infrastructure.
  • Optimization of Elman neural networks (ENN), deep neural networks (DNN), and artificial neural networks (ANN) for LSM is underexplored.
  • The application of Bayesian optimization and SHapley Additive exPlanations (SHAP) in LSM requires further investigation.

Purpose of the Study:

  • To optimize DNN, ENN, and ANN models using Bayesian optimization for robust landslide susceptibility mapping.
  • To derive SHAP values from optimized models to understand landslide-driving factors.
  • To compare the performance of optimized DNN, ENN, and ANN models for LSM.

Main Methods:

  • Bayesian optimization was employed to tune hyperparameters for DNN, ENN, and ANN models.
  • Six machine learning-based feature selection techniques were utilized to identify key landslide-influencing variables.
  • Model performance was validated using receiver operating characteristics curves, confusion matrices, and twelve error matrices.

Main Results:

  • Feature selection identified slope, elevation, rainfall, land use, and lineament density as critical factors influencing landslides.
  • The optimized deep neural network (DNN) model significantly outperformed ENN and ANN models in landslide susceptibility mapping.
  • The DNN model accurately classified large areas into very low and very high landslide susceptibility zones.

Conclusions:

  • Optimized deep neural networks (DNN) are highly effective for accurate landslide susceptibility mapping.
  • Key factors contributing to landslides include high elevation, specific land use types, and proximity to roads.
  • The study provides valuable insights for stakeholders to enhance landslide risk management strategies.