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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. [ABSTRACT FROM AUTHOR]

This paper aims at providing an answer as to whether generalization obtained with data-driven modelling can be used to gauge the plausibility of the physically based (PB) model's prediction. Two statistical models namely; Weight of Evidence (WofE) and Logistic Regression (LR), and a PB model using the infinite slope assumptions were evaluated and compared with respect to their abilities to predict susceptible areas to shallow landslides at the 1:10.000 urban scale. Threshold-dependent performance metrics showed that the three methods produced statistically comparable results in terms of success and prediction rates. However, with the Area Under the receiver operator Curve (AUC), statistical models are more accurate (88.7 and 84.6% for LR and WofE, respectively) than the PB model (only 69.8%). Nevertheless, in such data-sparse situation, the usual approaches for validation, i.e. comparing observed with predicted data, are insufficient, formal uncertainty analysis (UA) is a means for evaluating the validity and reliability of the model. We then refitted the PB model using a stochastic modification of the infinite slope stability model input scheme using Monte Carlo (MC) method backed with sensitivity analysis (SA). For statistical models, we used an informal Student t-test for estimating the certainty of the predicted probability (PP) at each location. Both modelling outputs independently show a high validity; and whereas the level of confidence in LR and WofE models remained the same after performance re-evaluation, the accuracy of the PB model showed an improvement (AUC = 72%). This result is reasonable and provides a further validation of PB model. So, in urban slope analysis, where PB diagnostic is necessary, statistical and PB modelling may play equally supportive roles in landslide hazard assessment. [ABSTRACT FROM AUTHOR]

The geomorphological control on landslides susceptibility maps (LSM) and frequency-area distributions (FAD) is under-investigated. Therefore, we investigate LSMs and FAD distributions in two contrasted landscapes of Taounate province using explanatory statistical techniques. In the geologically heterogenous context, our findings show that LSM spatial distributions for deep landslides are different to those of shallow processes, regardless of the LSM technique used. The Predictive factors also differ between both types of mass movements in this rugged and heterogenous zone. However, the opposite is observed in the geologically homogenous South, where shallow and deep landslides seem to be controlled by the same factors and tend to produce LSMs that are similarly distributed. This is further emphasized by the Area Under Curve (AUC) results which yield good to very good values in the South regardless of the types of landslides used for validation, with average to bad values in the North when shallow landslides are used to validate deep LSMs and vice versa. The same can be said about FADs, which are also sensitive to landscape variations. [ABSTRACT FROM AUTHOR]

The 590 km2 of the Western Rif (North of Morocco) is well known for active mass movements due to the strong climatic and anthropogenic aggressions in this area, where lithology and topographic relief are likely the phenomena that cause slope instability problems. In this study, we have focused on mass movements that have been occurred in the last two decades, namely landslides, complex slides and block slides. Over the years, this site and its surroundings have experienced repeated landslides. For this reason, landslides susceptibility mapping is mandatory to prevent natural risk and to better manage land-use. The application of weight of evidence method using remote sensing and geographical information systems (GIS) were used to generate a susceptibility map. Six conditioning factors were used in this study: lithology, distance to faults, distance to drainage, slope, aspect and hypsometry. A total of 126 mass movements locations were detected; 63 landslide location (50%) were randomly selected as input data for the entire process using the spatial data model (SDM) and the remaining locations were used for validation purposes. The final map of areas susceptible to mass movements has an accuracy of 95.3% and has been classified into four classes (very high, high, moderate and low). [ABSTRACT FROM AUTHOR]