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Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques

Author

Listed:
  • Hamid Reza Pourghasemi

    (Shiraz University)

  • Nitheshnirmal Sadhasivam

    (University of Twente)

  • Mahdis Amiri

    (Gorgan University of Agricultural Sciences and Natural Resources)

  • Saeedeh Eskandari

    (Agricultural Research Education and Extension Organization (AREEO))

  • M. Santosh

    (China University of Geosciences Beijing
    University of Adelaide)

Abstract

Landslides pose a serious risk to human life and the natural environment. Here, we compare machine learning algorithms including the generalized linear model (GLM), mixture discriminant analysis (MDA), boosted regression tree (BRT), and functional discriminant analysis (FDA) to evaluate the landslide exposure regions in Fars Province, comprising an area of approximately 7% of Iran. Initially, an aggregate of 179 historical landslide occurrences was prepared and partitioned. Subsequently, ten landslide conditioning factors (LCFs) were generated. The partial least squares algorithm was utilized to assess the significance of the LCFs with the help of a training dataset which indicated that distance from road had the maximum significance in forecasting landslides, followed by altitude (Al), lithological units, and slope degree. Finally, the LSMs generated using BRT, GLM, MDA, and FDA were validated and compared using cut-off reliant and independent validation measures. The results of the validation metrics showed that GLM and BRT had an AUC of 0.908, while FDA and MDA had AUCs of 0.858 and 0.821, respectively. The results from our case study can be utilized to develop strategies and plans to minimize the loss of human lives and the natural environment.

Suggested Citation

  • Hamid Reza Pourghasemi & Nitheshnirmal Sadhasivam & Mahdis Amiri & Saeedeh Eskandari & M. Santosh, 2021. "Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques," 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. 108(1), pages 1291-1316, August.
    • Handle: RePEc:spr:nathaz:v:108:y:2021:i:1:d:10.1007_s11069-021-04732-7
    DOI: 10.1007/s11069-021-04732-7
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    References listed on IDEAS

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    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Chang-Jo Chung & Andrea Fabbri, 2003. "Validation of Spatial Prediction Models for Landslide Hazard Mapping," 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. 30(3), pages 451-472, November.
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    Citations

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    Cited by:

    1. Batmyagmar Dashbold & L. Sebastian Bryson & Matthew M. Crawford, 2023. "Landslide hazard and susceptibility maps derived from satellite and remote sensing data using limit equilibrium analysis and machine learning model," 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 235-265, March.
    2. Faïla Benzenine & Mohamed Amine Allal & Chérifa Abdelbaki & Navneet Kumar & Mattheus Goosen & John Mwangi Gathenya, 2023. "Multi-Hazard Risk Assessment and Landslide Susceptibility Mapping: A Case Study from Bensekrane in Algeria," Sustainability, MDPI, vol. 15(3), pages 1-16, February.
    3. Xianyu Yu & Yang Xia & Jianguo Zhou & Weiwei Jiang, 2023. "Landslide Susceptibility Mapping Based on Multitemporal Remote Sensing Image Change Detection and Multiexponential Band Math," Sustainability, MDPI, vol. 15(3), pages 1-29, January.

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