Volume 2, Issue 1 — Year 2025 — Article e100028

ISSN (Online): 3115-8129 Biannually

GIS-based comparative landslide susceptibility mapping for Kelardasht county with ANN, SVM and RF models

Article Type: Research
Pages: e100028
DOI: https://doi.org/10.22034/CGEL.2.1.e100028
Crossmark
Open Access
Authors
Affiliations
  1. School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China
  2. School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China
  3. School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China
  4. School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, China
Corresponding author
Email: wangli0825@126.com
Received: 17 February 2025 / Accepted: 10 April 2025 / Published: 22 April 2025
Abstract

This study presents a GIS-based comparative analysis for landslide susceptibility mapping in Kelardasht County, utilizing artificial neural networks (ANN), support vector machines (SVM), and random forest (RF) models. The study identifies key triggering factors, including slope angle, aspect, lithology, land-use, proximity to faults, proximity to rivers, proximity to cities, proximity to occurred landslides, rainfall, and elevation, and 42 historical landslide records. The methodology involves integrating spatial data into a GIS framework, applying data preprocessing and feature selection techniques, followed by training and validation of the ANN, SVM, and RF models. Model performance was assessed using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the receiver operating characteristic (ROC) curve. The RF model demonstrated superior accuracy with the highest area under the curve (AUC) value, followed by ANN and SVM, indicating its robustness in identifying high-susceptibility zones. Verification results confirm the reliability of the models, providing precise susceptibility maps that classify the region into five risk categories: very high, high, moderate, low, and very low. The findings offer essential insights for regional planners and policymakers, enabling informed decisions on mitigation strategies and land-use planning to minimize landslide impacts. This comparative approach underscores the value of machine learning models in advancing landslide susceptibility assessment.

Keywords

Landslide susceptibility, Machine learning, Geohazards, SVM, ANN

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
  • RF model shows highest accuracy in landslide mapping
  • ANN and SVM provide reliable predictions
  • GIS maps classify regions into risk levels
References
  1. Abella C.E.A., Van Westen C.J. (2008). Qualitative landslide susceptibility assessment by multicriteria analysis: a case study from San Antonio del Sur, Guantanamo, Cuba. Geomorphology, 94(3-4), 453-466. https://doi.org/10.1016/j.geomorph.2006.10.038.
  2. Ado M., Amitab K., Maji A.K., Jasińska E., Gono R., Leonowicz Z., Jasiński M. (2022). Landslide susceptibility mapping using machine learning: A literature survey. Remote Sensing, 14(13), 3029. https://doi.org/10.3390/rs14133029.
  3. Ahmadlou M., Ghajari, Y.E., Karimi M. (2022). Enhanced classification and regression tree (CART) by genetic algorithm (GA) and grid search (GS) for flood susceptibility mapping and assessment. Geocarto International, 37(26), 13638-13657. https://doi.org/10.1080/10106049.2022.2082550.
  4. Arabameri A., Pradhan B., Rezaei K., Lee C.W. (2019). Assessment of landslide susceptibility using statistical-and artificial intelligence-based FR–RF integrated model and multiresolution DEMs. Remote Sensing, 11(9), 999. https://doi.org/10.3390/rs11090999.
  5. Azarafza M., Akgün H., Atkinson P.M., Derakhshani R. (2021). Deep learning-based landslide susceptibility mapping. Scientific Reports, 11(1), 24112. https://doi.org/10.1038/s41598-021-03585-1.
  6. Azarafza M., Ghazifard A., Akgün H., Asghari-Kaljahi E. (2017). Application of the Q-slope classification system for slope stability assessment of the south flank of the Assalouyeh anticline, South Pars Zone. Geotechnical Geology, 13(2), 82-90.
  7. Azarafza M., Ghazifard A., Akgün H., Asghari-Kaljahi E. (2018). Landslide susceptibility assessment of South Pars Special Zone, southwest Iran. Environmental Earth Sciences, 77(24), 805. https://doi.org/10.1007/s12665-018-7978-1.
  8. Broeckx J., Vanmaercke M., Duchateau R., Poesen J. (2018). A data-based landslide susceptibility map of Africa. Earth-Science Reviews, 185, 102-121. https://doi.org/10.1016/j.earscirev.2018.05.002.
  9. Dou J., Yamagishi H., Pourghasemi H.R., Yunus A.P., Song X., Xu Y., Zhu Z. (2015). An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Natural Hazards, 78, 1749-1776. https://doi.org/10.1007/s11069-015-1799-2.
  10. Ehteshami-Moinabadi M., Nasiri S. (2019). Geometrical and structural setting of landslide dams of the Central Alborz: a link between earthquakes and landslide damming. Bulletin of Engineering Geology and the Environment, 78, 69-88. https://doi.org/10.1007/s10064-017-1021-8.
  11. Ermini, L., Catani, F., & Casagli, N. (2005). Artificial neural networks applied to landslide susceptibility assessment. Geomorphology, 66(1-4), 327-343. https://doi.org/10.1016/j.geomorph.2004.09.025.
  12. Günther A., Reichenbach P., Malet J.P., Van Den Eeckhaut M., Hervás J., Dashwood C., Guzzetti F. (2013). Tier-based approaches for landslide susceptibility assessment in Europe. Landslides, 10, 529-546. https://doi.org/10.1007/s10346-012-0349-1.
  13. Hong H., Liu J., Zhu A.X. (2019). Landslide susceptibility evaluating using artificial intelligence method in the Youfang district (China). Environmental Earth Sciences, 78, 488. https://doi.org/10.1007/s12665-019-8415-9.
  14. Huang Y., Zhao L. (2018). Review on landslide susceptibility mapping using support vector machines. Catena, 165, 520-529. https://doi.org/10.1016/j.catena.2018.03.003.
  15. Kardavani P., Rad, A.F., Kavoosi B. (2014). Mazandaran province Geotourism. Journal of Tourism Hospitality Research, 3(1), 23-47. https://doi.org/20.1001.1.20089562.2014.3.1.2.2.
  16. Kavoosi M.A., Ezoji N. (2021). The Cenomanian heterozoan carbonates in the north-central Alborz, north-east Kelardasht, north Iran. Geological Quarterly, 65(3), 37-35.
  17. Kavzoglu T., Colkesen I., Sahin E.K. (2019). Machine learning techniques in landslide susceptibility mapping: a survey and a case study. Landslides: Theory, practice and modelling, pp. 283-301. https://doi.org/10.1007/978-3-319-77377-3_13.
  18. Lei X., Chen W., Pham B.T. (2020). Performance evaluation of GIS-based artificial intelligence approaches for landslide susceptibility modeling and spatial patterns analysis. ISPRS International Journal of Geo-Information, 9(7), 443. https://doi.org/10.3390/ijgi9070443.
  19. Pham B.T., Pradhan B., Bui D.T., Prakash I., Dholakia M.B. (2016). A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India). Environmental Modelling & Software, 84, 240-250. https://doi.org/10.1016/j.envsoft.2016.07.005.
  20. Qi T., Zhao Y., Meng X., Chen G., Dijkstra T. (2021). Ai-based susceptibility analysis of shallow landslides induced by heavy rainfall in Tianshui, China. Remote Sensing, 13(9), 1819. https://doi.org/10.3390/rs13091819.
  21. Sarkar S., Kanungo D.P. (2004). An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogrammetric Engineering & Remote Sensing, 70(5), 617-625. https://doi.org/10.14358/PERS.70.5.617.
  22. Thiery Y., Malet J.P., Sterlacchini S., Puissant A., Maquaire O. (2007). Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment. Geomorphology, 92(1-2), 38-59. https://doi.org/10.1016/j.geomorph.2007.02.020.
  23. Van Westen C.J., Rengers N., Soeters R. (2003). Use of geomorphological information in indirect landslide susceptibility assessment. Natural Hazards, 30, 399-419. https://doi.org/10.1023/B:NHAZ.0000007097.42735.9e.
  24. Wang H., Zhang L., Luo H., He J., Cheung R.W.M. (2021). AI-powered landslide susceptibility assessment in Hong Kong. Engineering Geology, 288, 106103. https://doi.org/10.1016/j.enggeo.2021.106103.
  25. Yong C., Jinlong D., Fei G., Bin T., Tao Z., Hao F., Qinghua Z. (2022). Review of landslide susceptibility assessment based on knowledge mapping. Stochastic Environmental Research and Risk Assessment, 36(9), 2399-2417. https://doi.org/10.1007/s00477-021-02165-z.
  26. Zhao P., Masoumi Z., Kalantari M., Aflaki M., Mansourian A. (2022). A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods. Remote Sensing, 14(1), 211. https://doi.org/10.3390/rs14010211.
How to cite
Abgrami, A., Zhang, W., Mao, H., & Wang, L. (2025). GIS-based comparative landslide susceptibility mapping for Kelardasht county with ANN, SVM and RF models. Civil and Geoengineering Letters, 2(1), e100028. https://doi.org/10.22034/CGEL.2.1.e100028
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