Machine learning and landslide studies: recent advances and applications

Faraz S. Tehrani, Michele Calvello (), Zhongqiang Liu (), Limin Zhang and Suzanne Lacasse
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Faraz S. Tehrani: Deltares (formerly)
Michele Calvello: University of Salerno
Zhongqiang Liu: Norwegian Geotechnical Institute
Limin Zhang: Hong Kong University of Science and Technology
Suzanne Lacasse: Norwegian Geotechnical Institute

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 114, issue 2, No 6, 1197-1245

Abstract: Abstract Upon the introduction of machine learning (ML) and its variants, in the form that we know today, to the landslide community, many studies have been carried out to explore the usefulness of ML in landslide research and to look at some classic landslide problems from an ML point of view. ML techniques, including deep learning methods, are becoming popular to model complex landslide problems and are starting to demonstrate promising predictive performance compared to conventional methods. Almost all the studies published in the literature in recent years belong to one of the following three broad categories: landslide detection and mapping, landslide spatial forecasting in the form of susceptibility mapping, and landslide temporal forecasting. In this paper, we present a brief overview of ML techniques, provide a general summary of the landslide studies conducted, in recent years, in the three above-mentioned categories, and make an attempt to critically evaluate the use of ML methods to model landslide processes. The paper also provides suggestions for future use of these powerful data-driven techniques in landslide studies.

Keywords: AI; ML; Landslide detection; Spatial forecasting; Temporal forecasting; Data-driven analysis (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-022-05423-7

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