Weights of evidence method for landslide susceptibility mapping in Tangier, Morocco

Abstract

Tangier region is known by a high density of mass movements which cause several human and economic losses. The goal of this paper is to assess the landslide susceptibility of Tangier using the Weight of Evidence method (WofE). The method is founded on the principle that an event (landslide) is more likely to occur based on the relationship between the presence or absence of a predictive variable (predisposing factors) and the occurrence of this event. The inventory, description and analysis of mass movements were prepared. Then the main factors governing their occurrence (lithology, fault, slope, elevation, exposure, drainage and land use) were mapped before applying WofE. Finally, the ROC curves were established and the areas under curves (AUC) were calculated to evaluate the degree of fit of the model and to choose the best landslide susceptibility zonation. The prediction accuracy was found to be 70%. Obtained susceptibility map shows that 60% of inventoried landslides are in the high to very high susceptibility zones, which is very satisfactory for the validation of the adopted model and the obtained results. These zones are mainly located in the N-E and E part of the Tangier region in the soft and fragile facies of the marls and clays of the Tangier unit, where landuse is characterized by dominance of arable and agricultural land (lack of forest cover). From a purely spatial point of view, the localization of these two classes of susceptibility is completely corresponding to the ground truth data, that is to say that all the environmental and anthropogenic conditions are in place for making this area prone to landslide hazards. The obtained map is a decision-making tool for presenting, comparing and discussing development and urban scenarios in Tangier. These results fall within the context of sustainable development and will help to mitigate the socio-economic impacts usually observed when landslides are triggered.

Susceptibility Mapping of Rainfall Induced Landslides Using Weight of Evidence Method in Jumre Khola Watershed of Pyuthan District, Nepal

“…Weight of Evidence is a bivariate analysis which contains predictive variable as independent variable and landslide occurrence as dependent variable (Bousta & Brahim, 2018).WOE was initially used for identification and exploration of mineral (Carter, 1994) but later it gained popularity for used in landslide assessment. The usefulness of a susceptibility map is greatly increased when it is divided into 5 zones: very low, low, moderate, high and very high susceptibility to land sliding (Fell et al, 2008).…”

Weights of evidence modeling for landslide susceptibility mapping of Kabi-Gebro locality, Gundomeskel area, Central Ethiopia

“…In this study, a bivariate statistical model known as weights of evidence model was used to prepare the landslide susceptibility map of the area. Several scholars applied weights of evidence model for landslide susceptibility mapping in different parts of the world with a high performance accuracy (Kumar et al 2008;Ghosh et al 2009;Blahut et al 2010;Piacentini et al 2012;Schicker and Moon 2012;Chen 2014;Sumatra et al 2015;Bousta and Ait 2018;Mersha and Meten 2020). Nowadays, weights of evidence model is the most commonly applied method for landslide susceptibility mapping as the model is simple, easy to use and less time consuming (Van Westen and Terlien 1996; Suzen and Doyuran 2004;Neuhäuser et al 2012).…”

Retrospective evaluation of landslide susceptibility maps and review of validation practice

“…At the same time, the development of powerful Geographic Information Systems (GIS), an ever-increasing resolution of remote sensing data as well as increasing computing power open new perspectives in landslide predictions. In this context, statistical methods moved increasingly into the scientific focus to analyze landslide susceptibility, especially in landslide-prone areas in developing countries (e.g., Bousta and Ait Brahim 2018;Lee et al 2018;Nsengiyumva et al 2018;Awawdeh et al 2018;Tasoglu et al 2016). Statistical methods are applicable on a regional or even national scale with comparably low financial, temporal, and personal expenses (e.g., Yesilnacar and Topal 2005).…”