Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Prediction of landslide susceptibility through ANN models optimized by evolutionary algorithms
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 17 February 2026

Prediction of landslide susceptibility through ANN models optimized by evolutionary algorithms

  • Mehmet Akif Cifci1,2,
  • Xin Hu3,
  • Batuhan Öney4,
  • Stanislav Misak5,
  • Hossein Moayedi6,7 &
  • …
  • Hossein Ahmadi Dehrashid8 

Scientific Reports , Article number:  (2026) Cite this article

  • 245 Accesses

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Mathematics and computing
  • Natural hazards

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.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Asadi, A., Baise, L. G., Chatterjee, S., Koch, M. & Moaveni, B. Regional landslide mapping model developed by a deep transfer learning framework using post-event optical imagery. Georisk: Assess. Manage. Risk Eng. Syst. Geohazards. 18 (1), 186–210 https://doi.org/10.1080/17499518.2024.2316265 (2024).

    Google Scholar 

  2. Liu, J. Q. et al. Prediction of water–mud inrush hazard from weathered granite tunnel by an improved seepage erosion model. Bull. Eng. Geol. Environ. 80 (12), 9249–9266 https://doi.org/10.1007/s10064-021-02480-3 (2021).

    Google Scholar 

  3. Lee, S. & Min, K. Statistical analysis of landslide susceptibility at Yongin. Korea Environ. Geol. 40 (9). https://doi.org/10.1007/s002540100310 (2001).

  4. Iwahashi, J., Watanabe, S. & Furuya, T. Mean slope-angle frequency distribution and size frequency distribution of landslide masses in Higashikubiki area. Japan Geomorphology. 50 (4), 349–364. https://doi.org/10.1016/S0169-555X(02)00222-2 (2003).

    Google Scholar 

  5. Ayalew, L. & Yamagishi, H. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko mountains. Cent. Japan Geomorphology. 65 (1–2), 15–31. https://doi.org/10.1016/j.geomorph.2004.06.010 (2005).

    Google Scholar 

  6. Bandibas, J. C. & Kohyama, K. An efficient artificial neural network training method through induced learning retardation: inhibited brain learning. Asian J. Geoinformatics. 1 (4), 45–55 (2001).

    Google Scholar 

  7. Zhao, T., Peng, H., Xu, L. & Sun, P. Statistical landslide susceptibility assessment using Bayesian logistic regression and Markov Chain Monte Carlo (MCMC) simulation with consideration of model class selection. Georisk: Assess. Manage. Risk Eng. Syst. Geohazards. 18 (1), 211–227 https://doi.org/10.1080/17499518.2023.2288600 (2024).

    Google Scholar 

  8. Fatahi, B. Uncertainty, modeling, and decision making in geotechnics. Georisk: Assess. Manage. Risk Eng. Syst. Geohazards. 18 (1), 314–316. https://doi.org/10.1080/17499518.2024.2314641 (2024).

    Google Scholar 

  9. Bozorgzadeh, N. & Feng, Y. Evaluation structures for machine learning models in geotechnical engineering. Georisk: Assess. Manage. Risk Eng. Syst. Geohazards. 18 (1), 52–59 https://doi.org/10.1080/17499518.2024.2313485 (2024).

    Google Scholar 

  10. Haykin, S. Neural Networks (MacMillan College Publ. Co., 1994).

  11. Ishibashi, H. Framework for risk assessment of economic loss from structures damaged by rainfall-induced landslides using machine learning. Georisk: Assess. Manage. Risk Eng. Syst. Geohazards. 18 (1), 228–243. https://doi.org/10.1080/17499518.2023.2288606 (2024).

    Google Scholar 

  12. Sethi, I. K. & Jain, A. K. Artificial Neural Networks and Statistical Pattern Recognition: Old and New Connections (Elsevier, 2014).

  13. Wang, P. S. & Guyon, I. Advances in Pattern Recognition Systems Using Neural Network Technologies (World Scientific, 1994).

  14. Melchiorre, C., Matteucci, M., Azzoni, A. & Zanchi, A. Artificial neural networks and cluster analysis. Landslide Susceptibility Zonation Geomorphology. 94 (3–4), 379–400. https://doi.org/10.1016/j.geomorph.2006.10.035 (2008).

    Google Scholar 

  15. Wang, Y. & Tian, H. M. Digital geotechnics: from data-driven site characterisation towards digital transformation and intelligence in geotechnical engineering. Georisk: Assess. Manage. Risk Eng. Syst. Geohazards. 18 (1), 8–32 https://doi.org/10.1080/17499518.2023.2278136 (2024).

    Google Scholar 

  16. Chen, W. et al. Landslide susceptibility mapping based on GIS and information value model for the Chencang district of Baoji, China. Arab. J. Geosci. 7 (11), 4499–4511. https://doi.org/10.1007/s12517-014-1369-z (2014).

    Google Scholar 

  17. Peng, L. et al. Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the three Gorges area. China Geomorphology. 204, 287–301. https://doi.org/10.1016/j.geomorph.2013.08.013 (2014).

    Google Scholar 

  18. Pham, B. T., Pradhan, B., Bui, D. T., Prakash, I. & Dholakia, M. A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India). Environ. Model. Softw. 84, 240–250. https://doi.org/10.1016/j.envsoft.2016.07.005 (2016).

    Google Scholar 

  19. Tien Bui, D., Tuan, T. A., Klempe, H., Pradhan, B. & Revhaug, I. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13, 361–378. https://doi.org/10.1007/s10346-015-0557-6 (2016).

    Google Scholar 

  20. Pradhan, B. & Lee, S. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling.Environ. Model. Softw. 25(6): 747–759. doi.https://doi.org/10.1016/j.envsoft.2009.10.016 (2010).

  21. Conforti, M., Pascale, S., Robustelli, G. & Sdao, F. Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo river catchment (northern Calabria, Italy). Catena 113, 236–250. https://doi.org/10.1016/j.catena.2013.08.006 (2014).

    Google Scholar 

  22. Feng, H., Yu, J., Zheng, J., Tang, X. & Peng, C. Evaluation of different models in rainfall-triggered landslide susceptibility mapping: a case study in Chunan, Southeast China. Environ. Earth Sci. 75, 1–15. https://doi.org/10.1007/s12665-016-6211-3 (2016).

    Google Scholar 

  23. Su, Q. et al. Comparative assessment of three nonlinear approaches for landslide susceptibility mapping in a coal mine area. ISPRS Int. J. Geo-Information. 6 (7), 228 (2017).

    Google Scholar 

  24. Micheletti, N., Foresti, L., Kanevski, M., Pedrazzini, A. & Jaboyedoff, M. Landslide susceptibility mapping using adaptive support vector machines and feature selection. Geophys. Res. Abstracts, 13, 99p (2011).

  25. Kohonen, T. An introduction to neural computing. Neural Netw. 1 (1), 3–16. https://doi.org/10.1016/0893-6080(88)90020-2 (1988).

    Google Scholar 

  26. Chou, J. S. & Bui, D. K. Modeling heating and cooling loads by artificial intelligence for energy-efficient Building design. Energy Build. 82, 437–446. https://doi.org/10.1016/j.enbuild.2014.07.036 (2014).

    Google Scholar 

  27. Castelli, M., Trujillo, L., Vanneschi, L. & Popovič, A. Prediction of energy performance of residential buildings: A genetic programming approach. Energy Build. 102, 67–74. https://doi.org/10.1016/j.enbuild.2015.05.013 (2015).

    Google Scholar 

  28. Hatamlou, A. Black hole: A new heuristic optimization approach for data clustering. Inf. Sci. 222, 175–184. https://doi.org/10.1016/j.ins.2012.08.023 (2013).

    Google Scholar 

  29. Yang, X. S. & Deb, S. Multiobjective cuckoo search for design optimization. Comput. Oper. Res. 40 (6), 1616–1624. https://doi.org/10.1016/j.cor.2011.09.026 (2013).

    Google Scholar 

  30. Rajabioun, R. Cuckoo optimization algorithm. Appl. Soft Comput. 11 (8), 5508–5518. https://doi.org/10.1016/j.asoc.2011.05.008 (2011).

    Google Scholar 

  31. Mirjalili, S., Mirjalili, S. M. & Hatamlou, A. Multiverse optimizer: a nature-inspired algorithm for global optimization. Neural Comput. Appl. 27, 495–513. https://doi.org/10.1007/s00521-015-1870-7 (2016).

    Google Scholar 

  32. Doğan, B. & Ölmez, T. A new metaheuristic for numerical function optimization: vortex search algorithm. Inf. Sci. 293, 125–145. https://doi.org/10.1016/j.ins.2014.08.053 (2015).

    Google Scholar 

  33. Dogan, B. & Ölmez, T. Modified off-lattice AB model for protein folding problem using the vortex search algorithm. Int. J. Mach. Learn. Comput. 5 (4), 329 (2015).

    Google Scholar 

  34. Altintasi, C., Aydin, O., Taplamacioglu, M. C. & Salor, O. Power system harmonic and interharmonic Estimation using vortex search algorithm. Electr. Power Syst. Res. 182, 106187. https://doi.org/10.1016/j.epsr.2019.106187 (2020).

    Google Scholar 

  35. Moayedi, H., Mehrabi, M., Mosallanezhad, M., Rashid, A. S. A. & Pradhan, B. Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng. Comput. 35 (3), 967–984 https://doi.org/10.1007/s00366-018-0644-0 (2019).

    Google Scholar 

  36. F Wieczorek, G. Preparing a detailed Landslide-Inventory map for hazard evaluation and reduction. Environ. Eng. Geoscience. xxi (3), 337–342. https://doi.org/10.2113/gseegeosci.xxi.3.337 (1984).

    Google Scholar 

  37. Chau, K. T. et al. Landslide hazard analysis for Hong Kong using landslide inventory and GIS. Comput. Geosci. 30(4): 429–443. doi.https://doi.org/10.1016/j.cageo.2003.08.013 (2004).

  38. Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F. & Reichenbach, P. Comparing Landslide Inventory Maps Geomorphology 94(3): 268–289. doi.https://doi.org/10.1016/j.geomorph.2006.09.023 (2008).

    Google Scholar 

  39. Calligaris, C., Poretti, G., Tariq, S. & Melis, M. T. First steps towards a landslide inventory map of the Central Karakoram National Park. Eur. J. Remote Sens. 46 (1), 272–287 https://doi.org/10.5721/EuJRS20134615 (2013).

    Google Scholar 

  40. Conforti, M., Muto, F., Rago, V. & Critelli, S. Landslide inventory map of north-eastern Calabria (South Italy). J. Maps. 10 (1), 90–102 https://doi.org/10.1080/17445647.2013.852142 (2014).

    Google Scholar 

  41. Xu, C. et al. Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Nat. Hazards. 68 (2), 883–900 https://doi.org/10.1007/s11069-013-0661-7 (2013).

    Google Scholar 

  42. Moosavi, V., Talebi, A. & Shirmohammadi, B. Producing a landslide inventory map using pixel-based and object-oriented approaches optimized. Taguchi Method Geomorphology. 204, 646–656. https://doi.org/10.1016/j.geomorph.2013.09.012 (2014).

    Google Scholar 

  43. Berberian, M. & King, G. C. P. Towards a paleogeography and tectonic evolution of Iran. Can. J. Earth Sci. 18 (2), 210–265 https://doi.org/10.1139/e81-019 (1981).

    Google Scholar 

  44. Zhang, L. & Wen, J. A systematic feature selection procedure for short-term data-driven Building energy forecasting model development. Energy Build. 183, 428–442. https://doi.org/10.1016/j.enbuild.2018.11.010 (2019).

    Google Scholar 

  45. Sun, J. Integration of random sample selection, support vector machines and ensembles for financial risk forecasting with an empirical analysis on the necessity of feature selection. Intell. Syst. Acc. Finance Manage. 19 (4), 229–246. https://doi.org/10.1002/isaf.1331 (2012).

    Google Scholar 

  46. Zhao, X. et al. A two-stage feature selection method with its application. Comput. Electr. Eng. 47, 114–125. https://doi.org/10.1016/j.compeleceng.2015.08.011 (2015).

    Google Scholar 

  47. Mafarja, M. M. & Mirjalili, S. Hybrid Whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260, 302–312. https://doi.org/10.1016/j.neucom.2017.04.053 (2017).

    Google Scholar 

  48. Zhang, H. et al. A combination of feature selection and random forest techniques to solve a problem related to blast-induced ground vibration. Appl. Sci. 10 (3), 869 (2020).

    Google Scholar 

  49. Zhang, Y., Liu, R., Wang, X., Chen, H. & Li, C. Boosted binary Harris Hawks optimizer and feature selection. Eng. Comput. 37 (4), 3741–3770. https://doi.org/10.1007/s00366-020-01028-5 (2021).

    Google Scholar 

  50. Taylor, D. W. Stability of Earth slopes. J. Boston Soc. Civil Eng. 24 (3), 197–247 (1937).

    Google Scholar 

  51. Doğan, B. & Ölmez, T. Vortex search algorithm for the analog active filter component selection problem. AEU-International J. Electron. Commun. 69 (9), 1243–1253. https://doi.org/10.1016/j.aeue.2015.05.005 (2015).

    Google Scholar 

  52. Khezri, S. et al. Prediction of landslides by machine learning algorithms and statistical methods in Iran. Environ. Earth Sci. 81 (11), 304. https://doi.org/10.1007/s12665-022-10388-8 (2022).

    Google Scholar 

Download references

Acknowledgements

We want to express our appreciation to all the participants, without whom this study would be impossible.

Funding

No funding was received for this article.

Author information

Authors and Affiliations

  1. Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Bandırma Onyedi Eylül University, Bandırma, Balıkesir, Turkey

    Mehmet Akif Cifci

  2. Engineering and Informatics Department, Klaipėdos Valstybinė Kolegija/Higher Education Institution, Klaipeda, 92294, Lithuania

    Mehmet Akif Cifci

  3. Zhejiang Forestry Resources Development Co., Ltd, 325005, Wenzhou, China

    Xin Hu

  4. Department of Electronics and Automation, Vocational School, Istinye University, Istanbul, 34396, Turkey

    Batuhan Öney

  5. ENET Centre, CEET, VSB-Technical University of Ostrava, Ostrava, 708 00, Czech Republic

    Stanislav Misak

  6. Institute of Research and Development, Duy Tan University, Da Nang, Vietnam

    Hossein Moayedi

  7. School of Engineering and Technology, Duy Tan University, Da Nang, Vietnam

    Hossein Moayedi

  8. Department of Human Geography, Faculty of Geography, University of Tehran, Tehran, Iran

    Hossein Ahmadi Dehrashid

Authors
  1. Mehmet Akif Cifci
    View author publications

    Search author on:PubMed Google Scholar

  2. Xin Hu
    View author publications

    Search author on:PubMed Google Scholar

  3. Batuhan Öney
    View author publications

    Search author on:PubMed Google Scholar

  4. Stanislav Misak
    View author publications

    Search author on:PubMed Google Scholar

  5. Hossein Moayedi
    View author publications

    Search author on:PubMed Google Scholar

  6. Hossein Ahmadi Dehrashid
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Mehmet Akif Cifci and Hossein Moayedi were responsible for conceptualization, methodology, formal analysis, addressing reviewer comments, and supervision. Investigation and results interpretation were carried out by Xin Hu, Batuhan Öney, and Stanislav Misak. The original draft preparation and writing, as well as the review and editing of the manuscript, were performed by Hossein Ahmadi Dehrashid.

Corresponding author

Correspondence to Hossein Ahmadi Dehrashid.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethical approval

All ethical responsibilities are considered regarding the publication of this paper.

Consent to participate

All authors have participated in the final version of the manuscript.

Consent to publish

All authors have read and agreed to the published version of the manuscript.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cifci, M.A., Hu, X., Öney, B. et al. Prediction of landslide susceptibility through ANN models optimized by evolutionary algorithms. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39458-8

Download citation

  • Received: 31 July 2025

  • Accepted: 05 February 2026

  • Published: 17 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-39458-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Landslide
  • Mapping
  • Optimization algorithm
  • Artificial neural network
  • Hybrid algorithms
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics