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A comparative study of different machine learning methods coupled with GIS for landslide susceptibility assessment: a case study of N’fis basin, Marrakesh High Atlas (Morocco)

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Abstract

Improving the predictive accuracy of models based on machine learning techniques for assessing landslide susceptibility is an area that attracts researchers’ attention. In this study, we applied, evaluated, and compared three machine learning models: random forest (RF), artificial neuron network (ANN), and decision tree (DT), for spatial prediction of landslide susceptibility in the N’fis watershed located in the High Atlas (Morocco). A database from an inventory of 156 historical landslides, randomly split into a training (70%) and validation (30%) set, was combined with thematic maps of fourteen causal factors (elevation, slope angle, slope aspect, curvature, lithology, land use, distance to faults, distance to rivers, distance to roads, topographic position index, topographic wetness index, precipitation, soil type, and normalized vegetation index) to build and validate the models. The performance of these models was evaluated using several statistical indices and the ROC curve method. The results show that all three models have worked well. The analysis of these results indicates that the RF model is the best performing (AUC = 96.75%), followed by ANN (AUC = 94.40%), and finally DT (AUC = 85.65%).

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Authors and Affiliations

  1. Laboratory of Georesources, Geoenvironment and Civil Engineering (L3G), Faculty of Sciences and Techniques, Cadi Ayyad University, Marrakesh, Morocco

    Hassan Ait Naceur, Brahim Igmoulan & Omar Bourouay

  2. Data Science for Sustainable Earth Laboratory (Data4Earth), Sultan Moulay Slimane University, Beni Mellal, Morocco

    Mustapha Namous & Mustapha Ouayah

  3. Faculté Des Sciences, 2390, 40000, SemlaliaMarrakech, BP, Morocco

    Mostafa Amrhar

  4. Laboratory Geosciences and Environmental Techniques, Faculty of Sciences El Jadida, Chouaïb Doukkali University, El Jadida, Morocco

    Mourad Jadoud

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  1. Hassan Ait Naceur
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  2. Brahim Igmoulan
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  3. Mustapha Namous
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  4. Mostafa Amrhar
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  5. Omar Bourouay
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Correspondence to Hassan Ait Naceur.

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Ait Naceur, H., Igmoulan, B., Namous, M. et al. A comparative study of different machine learning methods coupled with GIS for landslide susceptibility assessment: a case study of N’fis basin, Marrakesh High Atlas (Morocco). Arab J Geosci 15, 1100 (2022). https://doi.org/10.1007/s12517-022-10349-2

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