Landslide Susceptibility Mapping in the Municipality of Oudka, Northern Morocco: A Comparison Between Logistic Regression and Artificial Neural Networks Models

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.

Landslide Susceptibility Mapping Using Machine Learning Algorithms Study Case Al Hoceima Region, Northern Morocco

“…After reviewing 12 scientific papers published between 2017 and 2021 in different journals (Ayalew and Yamagishi, 2005;Benchelha et al, 2019;Byou et al, 2020;Can et al, 2021;Karakas et al, 2020;Lee et al, 2017;Muñoz et al, 2020;Onagh et al, 2012;Sevgen et al, 2019;Tien Bui et al, 2012;Yordanov and Brovelli, 2020;Youssef and Pourghasemi, 2021), 10 conditioning factors were selected in terms of disponibility and their higher influence on Landslides occurrence can be grouped into 3 groups: geological factor (including lithology, distance-tofault), topographical factors (including elevation, slope, aspect, curvature, profile curvature, plan curvature), anthropogenic (including distance to roads) and land used (Normalized Difference Vegetation Index NDVI). The processing of landslide conditioning factors was done by a spatial analysis tool (ArcGIS software) with a common pixel size of 30 m and a common spatial reference (Merchich North Morocco) and the Mediterranean Sea was excluded from the calculation.…”

“…This region is frequently subjected to heavy precipitation accompanied by instabilities related to tectonic movements. A recent study was conducted in this region (precisely the municipality of Oudka) using machine learning methods (Logistic Regression and Artificial Neural Network) by (Benchelha et al, 2019), which led to interesting results and assured the importance of machine learning with GIS and Remote Sensing in landslide susceptibility mapping.…”