IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v121y2025i7d10.1007_s11069-025-07132-3.html
   My bibliography  Save this article

Spatial prediction and mapping of landslide susceptibility using machine learning models

Author

Listed:
  • Yu Chen

    (Sichuan University)

Abstract

Spatial prediction and mapping of landslide susceptibility are crucial for landslide risk assessment and management. In this study, three different machine learning techniques, viz. multi-layer perceptron (MLP), self-organizing map (SOM), and classification tree analysis (CTA), are used to predict and map the landslide susceptibility in a typical landslide-prone mountainous region (Hanyuan, Southwest China). Initially, 104 identified historical landslides (centroid) for the study case as an inventory was mapped and randomly partitioned into training and test datasets in a 7:3 proportion. Fifteen conditioning factors were then chosen from different physical-geographical conditions and optimized using multi-collinearity analysis. Subsequently, MLP, SOM and CTA models were employed to carry out the spatial prediction of landslide susceptibility zones and generate corresponding landslide susceptibility maps (LSMs), further categorized into four susceptibility classes from low to very high. The performance estimation, validation and comparison of LSMs were conducted using the success rate (SR), prediction rate (PR) methods and statistical tests. The results reveal that MLP has the highest PR and SR (86.68%; 90.08%), followed by SOM (85.10%; 89.48%) and CTA (74.69%; 86.85%). All three models have very good or good capability to describe landslide susceptibility zones, and can be advanced ML-technique options for generating feasible LSMs. The resulting LSMs can effectively support the landslide risk management decision-making and land use planning formulation in the landslide-prone area.

Suggested Citation

  • Yu Chen, 2025. "Spatial prediction and mapping of landslide susceptibility using machine learning models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(7), pages 8367-8385, April.
  • Handle: RePEc:spr:nathaz:v:121:y:2025:i:7:d:10.1007_s11069-025-07132-3
    DOI: 10.1007/s11069-025-07132-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-025-07132-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11069-025-07132-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Peyman Yariyan & Ebrahim Omidvar & Foad Minaei & Rahim Ali Abbaspour & John P. Tiefenbacher, 2022. "An optimization on machine learning algorithms for mapping snow avalanche susceptibility," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(1), pages 79-114, March.
    2. Guilherme Garcia Oliveira & Luis Fernando Chimelo Ruiz & Laurindo Antonio Guasselli & Claus Haetinger, 2019. "Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(2), pages 1049-1073, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiang Zhang & Minghui Zhang & Xin Liu & Berhanu Keno Terfa & Won-Ho Nam & Xihui Gu & Xu Zhang & Chao Wang & Jian Yang & Peng Wang & Chenghong Hu & Wenkui Wu & Nengcheng Chen, 2024. "Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 11485-11525, October.
    2. Rui Yuan & Jing Chen, 2022. "A hybrid deep learning method for landslide susceptibility analysis with the application of InSAR data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(2), pages 1393-1426, November.
    3. Xiao-yan Huang & Li He & Hua-sheng Zhao & Ying Huang & Yu-shuang Wu, 2021. "Prediction model based on the Laplacian eigenmap method combined with a random forest algorithm for rainstorm satellite images during the first annual rainy season in South China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 331-353, May.
    4. Sukanta Malakar & Abhishek K. Rai & Arun K. Gupta, 2023. "Earthquake risk mapping in the Himalayas by integrated analytical hierarchy process, entropy with neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(1), pages 951-975, March.
    5. Mária Barančoková & Matej Šošovička & Peter Barančok & Peter Barančok, 2021. "Predictive Modelling of Landslide Susceptibility in the Western Carpathian Flysch Zone," Land, MDPI, vol. 10(12), pages 1-28, December.
    6. Paulo Rodolpho Pereira Hader & Fábio Augusto Gomes Vieira Reis & Anna Silvia Palcheco Peixoto, 2022. "Landslide risk assessment considering socionatural factors: methodology and application to Cubatão municipality, São Paulo, Brazil," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(2), pages 1273-1304, January.
    7. Youssef El Miloudi & Younes El Kharim & Ali Bounab & Rachid El Hamdouni, 2024. "Effect of Rockfall Spatial Representation on the Accuracy and Reliability of Susceptibility Models (The Case of the Haouz Dorsale Calcaire, Morocco)," Land, MDPI, vol. 13(2), pages 1-16, February.
    8. Tingyu Zhang & Quan Fu & Chao Li & Fangfang Liu & Huanyuan Wang & Ling Han & Renata Pacheco Quevedo & Tianqing Chen & Na Lei, 2022. "Modeling landslide susceptibility using data mining techniques of kernel logistic regression, fuzzy unordered rule induction algorithm, SysFor and random forest," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 3327-3358, December.
    9. Hamid Reza Pourghasemi & Soheila Pouyan & Mojgan Bordbar & Foroogh Golkar & John J. Clague, 2023. "Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3797-3816, April.
    10. Xudong Hu & Hongbo Mei & Han Zhang & Yuanyuan Li & Mengdi Li, 2021. "Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(2), pages 1663-1689, January.
    11. Hüseyin Akay, 2022. "Towards Linking the Sustainable Development Goals and a Novel-Proposed Snow Avalanche Susceptibility Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6205-6222, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:121:y:2025:i:7:d:10.1007_s11069-025-07132-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.