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.
The main objective of this study is destined to combine the Analytical Hierarchy Process (AHP), Weight of Evidence (WOE), Logistic Regression (LR) methods and geographic information system (GIS) to predict landslide susceptibility of the Echorfa region (northwestern of Algeria). Nine factors such as slope, aspect, lithology, distance to faults, lineaments density, distance to the streams, precipitations, land use and altitude are included in landslide susceptibility evaluation process. A detailed landslide inventory map established by satellite images and filed surveys. Three landslide susceptibility maps are established using the different statistical models. Five landslide susceptibility categories are generated by the GSI classification nil, low, moderate, high and very high susceptibility. The performance of the different models in landslide susceptibility is calculate based in the area under curve of the Receiver Operating Characteristic (ROC) which give a satisfactory result, The results showed that the weight of evidence method is more performance than the two other techniques. The produced landslide susceptibility maps provide important spatial information about landslide prone area, where the constructed map's content will help the decision makers in land use planning.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.