Delineation of landslide susceptible zones using Frequency Ratio (FR) and Shannon Entropy (SE) models in northern Rif, Morocco

This study describes the findings of landslide susceptibility modelling and hazard analysis in the coastline Mediterranean between Tetouan-Bou Ahmed and its hinterlands North Morocco. The study was carried out using Frequency Ratio (FR) and Shannon Entropy (SE) models with the aid of GIS tools and remote sensing data sources supported by extensive field surveys. A methodology was developed for modelling and identifying the landslide susceptible zones and for generating an updated landslide inventory map to delineate the most sensitive landslide prone areas as well as to predict and reduce their impacts. For building these models, a total of 905 landslide incidences and eleven main landslide causative factors were used based on multi-collinearity diagnosis test. The validation of the model results showed good prediction ability (>76%) for both the models. However, the accuracy prediction indicated that the FR is about 3% more precise than SE model in landslide susceptibility delineation. Furthermore, more than 60% of the area was found as high risk zone that is predicted highly susceptible to the landsliding hazards under suitable triggering factors. The findings of this study constitutes a major and suitable database for local and national authorities for providing stratigies for landslide hazard mitigation and making better policies for sustainable development in the region. Sustainable adaptive solutions and measures are required to prevent the stability of this mountainous region which is under the impact of wide anthropogenic activities for developmental purposes

Landslide susceptibility mapping using GIS-based bivariate models in the Rif chain (northernmost Morocco)

The coastline between Tetouan and Bou Ahmed in the northernmost Rif of Morocco and its hinterland has become immensely hazardous due to frequent triggering of diversified landslides from last two decades. This paper describes the potential application of a set of multisource data and the GIS platform for zoning and identifying anomalous areas prone to landsliding and its associated landslide hazards. For this purpose, Information value (IV), Statistical index SI (Wi), Weighting factors (WF) and Evidential belief function (EBF) models have been used in this study. Eleven conditioning factors such as elevation, slope, aspect, curvature, shaded/relief, proximity to streams, proximity to faults, proximity to roads, land use, lithology, annual rainfall and an inventory of 905 unstable spots were used to develop the spatial database for landslides susceptibility mapping (LSM). The factors have been used after a test of multi-collinearity. The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) methods were used for validation of the LSM. The AUC results showed good prediction accuracy for all models with a prediction rate of 78% (IV), 77% (SI), 73% (WF) and 70% (EBF) respectively. However, the results indicated that comparatively, the IV model followed by WI model is more precise and accurate for landslides susceptibility mapping than other models. According to the presented models, about 64% of the study area is located in high to very high landslide susceptible zone. The findings presented in this study are imperatively valuable especially wherein large development projects and land use planning activities are going on