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Articles | Volume XLII-4/W12
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W12, 41–49, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W12-41-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W12, 41–49, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W12-41-2019

  21 Feb 2019

21 Feb 2019

LANDSLIDE SUSCEPTIBILITY MAPPING IN THE MUNICIPALITY OF OUDKA, NORTHERN MOROCCO: A COMPARISON BETWEEN LOGISTIC REGRESSION AND ARTIFICIAL NEURAL NETWORKS MODELS

S. Benchelha1, H. Chennaoui Aoudjehane1, M. Hakdaoui2, R. El Hamdouni3, H. Mansouri4, T. Benchelha1, M. Layelmam5, and M. Alaoui6 S. Benchelha et al.
  • 1GAIA Laboratory, Hassan II University of Casablanca, Faculty of Sciences Aïn Chock, Morocco
  • 2LGAGE Laboratory, Hassan II University of Casablanca, Faculty of Sciences Ben M'sik, Morocco
  • 3Civil Engineering Department, Granada University, Granada, Spain
  • 4Laboratoire Public d'Essai et d'Etudes (LPEE), Casablanca, Morocco
  • 5Hassan II Agronomic and Veterinary Institute, Rabat, Morocco
  • 6Laboratory of Management and Valorization of Natural Resources, Faculty of Science and Technology, Sultan Moulay Slimane University, Beni Mellal, Morocco

Keywords: Landslide susceptibility mapping, regression logistic, Artificial Neural Networks, Oudka, Taounate

Abstract. The Rif is among the areas of Morocco most susceptible to landslides, because of the existence of relatively young reliefs marked by a very important dynamics compared to other regions. These landslides are one of the most serious problems on many levels: social, economic and environmental. The increase in the frequency and impact of landslides over the past decade has demonstrated the need for an in-depth study of these phenomena, allowing the identification of areas susceptible to landslides.

The main objective of this study is to identify the optimal method for the mapping of the area susceptible to landslides in municipality of Oudka. This area has been marked by the largest landslide in the region, caused by heavy rainfall in 2013. Two Statistical Methods i) Regression Logistics (LR) ii) Artificial Neural Networks (ANN), were used to create a landslide susceptibility map. The realization of this susceptibility map required, first, the mapping of old landslides by the aerial photography, the data of the geological map and by the data obtained using field surveys using GPS. A total of 105 landslides were mapped from these various sources. 50% of this database was used for model building and 50% for validation. Eight independent landslide factors are exploited to detect the most sensitive areas: altitude, slope, aspect, distance of faults, distance streams, distance from roads, lithology and vegetation index (NDVI).

The results of the landslide susceptibility analysis were verified using success and prediction rates. The success rate (AUC = 0.918) and the prediction rate (AUC = 0.901) of the LR model is higher than that of the ANN model (success rate (AUC = 0.886) and prediction rate (AUC = 0.877).

These results indicate that the Regression Logistic (LR) model is the best model for determining landslide susceptibility in the study area.