Landslide Hazard Assessment in the Heterogeneous Geomorphological and Environmental Context of the Rif Region, Morocco – A Machine Learning Approach

Hamidi, Maryem; Bouramtane, Tarik; Abraham, Shiny; Kacimi, Ilias; Barbiero, Laurent; Kassou, Nadia; Valles, Vincent; Levy, Gad

doi:10.12912/27197050/172569

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Artykuł - szczegóły

Czasopismo

Ecological Engineering & Environmental Technology

2023 | Vol. 24, iss. 8 | 272--292

Tytuł artykułu

Landslide Hazard Assessment in the Heterogeneous Geomorphological and Environmental Context of the Rif Region, Morocco – A Machine Learning Approach

Autorzy

Hamidi, Maryem , Bouramtane, Tarik , Abraham, Shiny , Kacimi, Ilias , Barbiero, Laurent , Kassou, Nadia , Valles, Vincent , Levy, Gad

Treść / Zawartość

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Warianty tytułu

Języki publikacji

Abstrakty

Landslides are considered to be one of the most significant and critical natural hazards in the heterogeneous geomor-phological setting of the Rif region of Morocco. Despite the high susceptibility to landslides, the region lacks detailed studies. Therefore, this research introduces four advanced machine learning methods, namely Support Vector Machine (SVM), Classification and Regression Trees (CART), Multivariate Discriminant Analysis (MDA), and Logistic Regression (LR), to perform landslide susceptibility mapping, as well as study of the connection between landslide occurrence and the complex regional geo-environmental context of Taounate province. Fifteen causative factors were extracted, and 255 landslide events were identified through fieldwork and satellite imagery analysis. All models performed very well (AUC > 0.954), while the CART model performed the best (AUC= 0.971). However, SVM demonstrated superior performance compared to other methods, achieving the highest accuracy (89.92%) and F1-measure (81.66%) scores on the training data, and the highest accuracy (83.01%), precision (81.74%), and specificity (79.46%) scores on the test data. The results do not necessarily indicate that LR and MDA have the lowest predictive ability, as they demonstrated high accuracy in terms of AUC and in some classification tasks. Moreover, they provide the significant advantage of easy interpretation of the geo-environmental processes that control landslides. Rainfall is the primary triggering factor of landslides in the study area. The majority of landslides occurred on slopes, particularly those located along rivers and faults, suggesting that landslides in the region are closely associated with active tectonics and precipitation. All four models predicted similar spatial distribution patterns in landslide susceptibility. The results showed that almost half of the area mainly in the north and northwest, has a very high susceptibility to landslides. The findings provide valuable references for land use management and the implementation of effective measures for landslide prevention.

Słowa kluczowe

landslide machine learning Morocco susceptibility mapping remote sensing Rif

Wydawca

Czasopismo

Ecological Engineering & Environmental Technology

Rocznik

2023

Tom

Vol. 24, iss. 8

Strony

272--292

Opis fizyczny

Bibliogr. 76 poz., rys., tab.

Twórcy

autor

Hamidi, Maryem

Geosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University, Avenue Ibn Batouta, Rabat 10100, Morocco, maryem_hamidi@um5.ac.ma

autor

Bouramtane, Tarik

Geosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University, Avenue Ibn Batouta, Rabat 10100, Morocco

autor

Abraham, Shiny

Electrical and Computer Engineering Department, Seattle University, Seattle, WA 98122, USA

autor

Kacimi, Ilias

Geosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University, Avenue Ibn Batouta, Rabat 10100, Morocco

autor

Barbiero, Laurent

Géoscience Environnement Toulouse, IRD, CNRS, UPS, OMP, Mixed Research Unit 5563, 14 Av. E. Belin, 31400 Toulouse, France

autor

Kassou, Nadia

Geosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University, Avenue Ibn Batouta, Rabat 10100, Morocco

autor

Valles, Vincent

Mixed Research Unit EMMAH (Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes), Hydrogeology Laboratory, Avignon University, 84916 Avignon, France

autor

Levy, Gad

NorthWest Research Associates & Pan Ocean Remote Sensing Association, Seattle, WA 98105, USA

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Typ dokumentu

Bibliografia

Identyfikatory

DOI

10.12912/27197050/172569

Identyfikator YADDA

bwmeta1.element.baztech-9f3f9449-e9a8-494e-bd95-ea20087ee36e