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Performance evaluation of machine learning and statistical techniques for modelling landslide susceptibility with limited field data

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Abstract

Due to the unique blend of physiography, climate, and socio-economic factors, the windward slopes of the southern Western Ghats (WG) are highly susceptible to the occurrence of landslides. The present study compares the efficiency of advanced deep learning and machine learning techniques, such as deep neural network (DNN), and random forest (RF) with conventional methods, viz., frequency ratio (FR) and analytical hierarchy process (AHP) method for mapping the landslide susceptibility in a highland region of the western slopes of the southern WG, where the field observations are limited. Various factors controlling the occurrence of landslides, such as lithology, geomorphology, land use/ land cover, soil texture, proximity to lineaments, roads, and streams, slope angle, rainfall, topographic wetness index (TWI) and curvature are used for the landslide susceptibility mapping. The results of the present study indicate comparable performances between machine learning models and conventional approaches in field data-scarce regions. Moreover, this study has extended implications in landslide risk management of the southern WG in the context of repeated extreme rainfall events.

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Authors and Affiliations

  1. Department of Climate Variability and Aquatic Ecosystems, Kerala University of Fisheries and Ocean Studies (KUFOS), Kochi, 682 508, Kerala, India

    Ashokan Laila Achu & Girish Gopinath

  2. Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI, 49931, USA

    Jobin Thomas

  3. Department of Geology, University of Kerala, Thiruvananthapuram, 695 581, Kerala, India

    Chandrika Dhanapalan Aju

  4. Geological Survey of India (GSI), Kolkata, India

    Praveen Kolappapillai Remani

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Achu, A.L., Thomas, J., Aju, C.D. et al. Performance evaluation of machine learning and statistical techniques for modelling landslide susceptibility with limited field data. Earth Sci Inform 16, 1025–1039 (2023). https://doi.org/10.1007/s12145-022-00910-8

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