Volume 2, Issue 2 — Year 2025 — Article e100039

ISSN (Online): 3115-8129 Biannually

Landslide Susceptibility Analysis using Artificial Neural Networks for Chalus County, Iran

Article Type: Case Study
Pages: e100039
DOI: https://doi.org/10.22034/CGEL.2.2.e100039
Crossmark
Open Access
Authors
Affiliations
  1. Departmen of Geographic Information Science, Chengdu University of Information Technology, Chengdu 610225, China
  2. Departmen of Geographic Information Science, Chengdu University of Information Technology, Chengdu 610225, China
  3. Departmen of Geographic Information Science, Chengdu University of Information Technology, Chengdu 610225, China
  4. Faculty of Basic Science, Maragheh University of Technology, Maragheh 5518183111, Iran
Corresponding author
Email: mj.moghadam@maragheh-sanati.ac.ir
Received: 18 August 2025 / Accepted: 19 September 2025 / Published: 19 October 2025
Abstract

Landslides are among the most critical natural hazards, causing significant environmental and economic damage. This study focuses on landslide susceptibility analysis in Chalus County, Iran, using Artificial Neural Networks (ANNs) to predict high-risk areas. A dataset of 77 recorded historical landslides was compiled through field surveys, remote sensing, and satellite imagery analysis. Various conditioning factors, including elevation, geology, slope angle, rainfall, temperature, aspect, NDVI (normalized difference vegetation index), weathering, distance to cities, distance to landslides, distance to rivers, distance to roads, and distance to faults, were integrated into a GIS-based modeling framework. The ANN model was trained using a dataset divided into training and validation subsets, ensuring robust predictive performance. The results demonstrated that ANNs effectively identify landslide-prone regions, with high accuracy in distinguishing between stable and unstable areas. The susceptibility map produced highlights that northern and mountainous regions of Chalus County are highly vulnerable to landslides, primarily due to steep slopes, heavy precipitation, and geological instability. Model validation using statistical accuracy measures, including the Area Under the Curve (AUC), precision, and recall, confirmed the reliability of the predictions. The findings of this study provide valuable insights for urban planners, geologists, and policymakers in risk assessment and land-use planning. Implementing these results can support early warning systems and mitigation strategies to minimize landslide-related hazards in the region. Future research should explore hybrid AI models and additional geospatial datasets to enhance predictive capabilities further.

Keywords

Landslide susceptibility, Artificial intelligence, Neural networks, Machine learning, ArcGIS

Data availability statement

The data supporting the findings of this study are available within article. No publicly archived dataset was generated.

Funding

This research received no external funding.

Highlights
  • ANN predicts landslide susceptibility accurately
  • High risk in northern mountainous areas
  • GIS mapping supports planning and mitigation
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How to cite
Ebadati, M., Sun, F., Lee, Y., & Moghadam, M. J. (2025). Landslide Susceptibility Analysis using Artificial Neural Networks for Chalus County, Iran. Civil and Geoengineering Letters, 2(2), e100039. https://doi.org/10.22034/CGEL.2.2.e100039
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