Abstract:

Geological hazards present one of the most important constraints for the development of the Arzew sector (Oran province), North Western of Algeria. Landslides are considered us one of the most common phenomena in the study area and especially in the hilly area. For minimizing and reducing the consequences of this problem, it is necessary to carry out preliminary studies on the cartography of the different zones exposed to the slope instability phenomena. The main objective of this study is to perform the landslide susceptibility mapping by statistical models and GIS techniques for the Arzew area. To achieve this goal, an analytical approach was carried out. Firstly, a landslide inventory map was prepared using previous inventory maps, satellite images, aerial photos and field surveys. Secondly seven conditioning factors such as slope degree, aspect, lithology, land use, distance to the streams, distance to the road and altitude were exploited to assess landslide susceptibility. Thirdly, the weight value for each class of the conditioning factors was determined using Frequency Ratio (FR) and Information Value (IV) models based in GIS functionalities. Consequently, Landslide Susceptibility Maps (LSMs) were produced by the classification process of the global Landslide Susceptibility Indexes (LSIs) into five classes. Finally, for experiment verification, the LSMs obtained with the FR and IV models were confirmed comparing LSMs with landslide inventory map using both the Receiver Operating Characteristics (ROC) and the Seed Cell Area Index (SCAI) models. The area under curve (AUC) results, demonstrate that the IV method more performance (89.03%) for LSM than FR method (85.57%). Furthermore, the validation results using SCAI   also confirmed that the IV model was more accurate than FR model. The models employed in this study are capable to resolve the issue of the landslide susceptibility of the study area. The produced susceptibility maps can be used for future land use planning and can be considered as a powerful tool to resolve the spatial distribution of the risk associated to landslides.

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