Abstract
This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco. Landslides hazard is the most frequent phenomenon in this part of the state due to its mountainous precarious environment. The abundance of rainfall makes this area suffer mass movements led to a notable adverse impact on the nearby settlements and infrastructures. There were 93 identified landslide scars. Landslide inventories were collected from Google Earth image interpretations. They were prepared out of landslide events in the past, and future landslide occurrence was predicted by correlating landslide predisposing factors. In this paper, landslide inventories are divided into two groups, one for landslide training and the other for validation. The Landslide Susceptibility Map (LSM) is prepared by Logistic Regression (LR) Statistical Method. Lithology, stream density, land use, slope curvature, elevation, topographic wetness index, slope aspect, and slope angle were used as conditioning factors. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was employed to examine the performance of the model. In the analysis, the LR model results in 96% accuracy in the AUC. The LSM consists of the predicted landslide area. Hence it can be used to reduce the potential hazard linked with the landslides in the Sahla watershed area in Rif Mountains in northern Morocco.
Highlights
This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco
It is expected that during the process, many conditioning factors affecting slope instability in the Rif mountains will be known, giving land-use planners working on landslides the ability to make appropriate decisions based on the quantify analyses of the the spatial probability of landslide hazards in the Sahla watershed with the use of Logistic Regression (LR)
The validation group of landslides, 50% (Figure 13), logistic regression susceptibility map obtained from the initial modeling and the Receiver Operating Characteristic (ROC) curve was developed by computing the background values with the susceptibility map as input
Summary
This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco. Mass movements are the most frequent natural hazards that affect large areas of the Rif mountains region in Northern Morocco, mostly triggered by heavy rainfall. It is one of the most reoccurring phenomena along with the Mountains chain threatening infrastructure and human properties. It is expected that during the process, many conditioning factors affecting slope instability in the Rif mountains will be known, giving land-use planners working on landslides the ability to make appropriate decisions based on the quantify analyses of the the spatial probability (susceptibility) of landslide hazards in the Sahla watershed with the use of LR. Multivariate statistical model in order To build a consistent landslide inventory for the study area using aerial photographs, satellite images, literature review, and field survey cartography
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