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<p>Landslide susceptibility mapping is a critical task for risk management, yet many existing approaches struggle with limited accuracy and model instability. To address these challenges, this study develops a hybrid Artificial Neural Network (ANN) framework optimized with four metaheuristic algorithms (BHA, COA, MVO, and VSA). The case study is conducted in East Azerbaijan Province, Iran, a region with sufficient landslide records for robust testing. The results show that the optimized ANN models achieved strong predictive performance, with Area Under the Curve (AUC) values ​​exceeding 0.97 across training datasets. Among them, the MVO-MLP and COA-MLP models yielded the highest accuracy, highlighting the advantage of optimization in enhancing model robustness. Overall, the developed models predict landslide occurrence with more than 80% accuracy. These findings suggest that integrating optimization algorithms with neural networks provides a reliable, cost-effective approach for spatial modeling of landslide susceptibility. Furthermore, the proposed framework offers valuable insights for disaster preparedness, risk reduction, and emergency management strategies.</p>

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Prediction of landslide susceptibility through ANN models optimized by evolutionary algorithms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月17日

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  • Mehmet Akif Cifci,
  • Xin Hu,
  • Batuhan Öney,
  • Stanislav Misak,
  • Hossein Moayedi,
  • Hossein Ahmadi Dehrashid

摘要

Landslide susceptibility mapping is a critical task for risk management, yet many existing approaches struggle with limited accuracy and model instability. To address these challenges, this study develops a hybrid Artificial Neural Network (ANN) framework optimized with four metaheuristic algorithms (BHA, COA, MVO, and VSA). The case study is conducted in East Azerbaijan Province, Iran, a region with sufficient landslide records for robust testing. The results show that the optimized ANN models achieved strong predictive performance, with Area Under the Curve (AUC) values ​​exceeding 0.97 across training datasets. Among them, the MVO-MLP and COA-MLP models yielded the highest accuracy, highlighting the advantage of optimization in enhancing model robustness. Overall, the developed models predict landslide occurrence with more than 80% accuracy. These findings suggest that integrating optimization algorithms with neural networks provides a reliable, cost-effective approach for spatial modeling of landslide susceptibility. Furthermore, the proposed framework offers valuable insights for disaster preparedness, risk reduction, and emergency management strategies.