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

Hamid Reza Pourghasemi (), Nitheshnirmal Sadhasivam, Mahdis Amiri, Saeedeh Eskandari and M. Santosh
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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

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 108, issue 1, No 56, 1316 pages

Abstract: 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.

Keywords: Partial least square; Landslides; Functional discriminant analysis; Mixture discriminant analysis; Boosted regression tree; Generalized linear model (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11069-021-04732-7

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