GIS-multicriteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco)

Abstract

Background: High basin of Oum Er Rbia River, which is located in Middle Atlas Mountain region, is prone to landslide problems due to the geological features combined with the climate change and human activities. The present work including inventory mapping was conducted to establish landslide susceptibility map using GIS-based spatial multicriteria approach. Eight landslide-related factors, including land cover, lithology, distance to road, distance to fault, distance to drainage network, elevation, aspect and slope gradient, were selected for the present assessment. Weight for each factor is assigned using Analytic Hierarchy Process (AHP) depending on its influence on the landslide occurrence. The landslide susceptibility map was derived using weighted overlay method and categorized into five susceptible classes namely, very low (VL), low (L), moderate (M), high (H). Result: The results revealed that 30.16% of the study area is at very low risk, 12.66% at low risk, 25.75% of moderate risk, 22.59% of high risk and 9.11% of very high risk area coverage. The very high landslide vulnerability zones are more common within the river valleys on steep side slopes. Most landslides also involve rocks belonging to the Triassic weathered marl and clay-rich formation. Moreover, human activities namely the construction and the expansion of agricultural lands into forests intervene in inducing landslides through altering the slope stability along the river banks. Lastly, effectiveness of these results was checked by computing the area under ROC curve (AUC) that showed a satisfactory result of 76.7%. Conclusions: The landslide susceptibility map of the Oum Er Rbia high basin provides valuable information about present and future landslides, which makes it viable. Such map may be helpful for planners and decision makers for land-use planning and slope management.

Temporal Probability Assessment and Its Use in Landslide Susceptibility Mapping for Eastern Bhutan

“…The decision analysis technique combines primary-and secondary-level weights for every causative factor, where primary weights are similar to the bivariate approach and secondary weights are expert-opinion-based. For secondary weights, the analytic hierarchy process (AHP) has become popular and has been successfully applied for decision-making systems [60,61]. AHP uses a pairwise relative comparison between every landslide-causative factor.…”

Landslide Susceptibility Mapping using Statistical Methods in Uatzau Catchment Area, Northwestern Ethiopia

“…The remaining 23.8%, 31.8%, and 26.3% of landslides fall into moderate, high, and very high landslide susceptibility classes. A landslide susceptibility map produced using certainty factor model (Table 3), very low and low susceptibility classes cover 17.8% and 31.0% of the total study area, however, 28.8%, 19.0% and 3.4% of the total area fall into moderate, high and very high landslide susceptibility classes, respectively. As indicated in Table 3, 4.7% and 12.3% of the landslide fall in very low and low susceptibility classes of the study area, respectively.…”

Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

“…Considering 13 landslide-related factors, they achieved the proposed maps with 78.53%, 79.35%, and 79.40% prediction accuracies, respectively for WoE, SI, and FR models. Analytical hierarchy process (AHP) is another popular method employed in landslide susceptibility mapping [14,15]. Yan, et al [16] used the integration of AHP with normalized FR with the cloud model for landslide susceptibility assessment.…”