Prediction of landslide susceptibility through ANN models optimized by evolutionary algorithms

Cifci M. A., Hu X., Öney B., Misak S., Moayedi H., Dehrashid H. A.

Scientific Reports, vol.16, no.1, 2026 (SCI-Expanded, Scopus) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 16 Issue: 1
  • Publication Date: 2026
  • Doi Number: 10.1038/s41598-026-39458-8
  • Journal Name: Scientific Reports
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, MEDLINE, Directory of Open Access Journals
  • Keywords: Artificial neural network, Hybrid algorithms, Landslide, Mapping, Optimization algorithm
  • Istanbul University Affiliated: Yes

Abstract

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.