Mass movements susceptibility mapping by using heuristic approach. Case study: province of Tétouan (North of Morocco)

Abstract

This research paper aims to model Mass Movements Susceptibility (MMS) in the province of Tétouan. First, we identified the characteristics and spatial mapping of the different types of MM (collapse, mudflows, and complex landslides) by means of the interpretation of satellite images and from fieldwork. Subsequently, we selected the predictive parameters controlling the occurrence of MM e.g. lithology, land use, fault density, hydrographic network density, slope degrees, slope aspects, and elevation. We used the heuristic method for Modeling Mass Movements Susceptibility (MMMS). The choice of this method compared to other methods (fractal, factorial, and neurons) is justified by the possibilities of intervention and the judgment of the expert who relies on the ground truth to select the parameters, to identify the classes, and to assign the weights to each one; unlike to other methods with steps that are done automatically and randomly. The results of the validation of the susceptibility map correspond to 70% compared to the field data and it includes five susceptibility classes (not susceptible, low, moderate, high, and very high). Indeed, the originality of this paper relies on the fact that the creation of our susceptibility map will eventually indicate the areas of roads, dwellings, the extension of urbanization, and dams, which are located in areas at risk of MM. Our map is also a powerful decision-making tool to conduct management plans and to guide the selection of sites to build new projects; which help mitigate the socio-economic impacts usually encountered when mass movements in Tétouan province are triggered.

Short-term post-fire structural and compositional habitat resilience in pine plantations

“…The native vegetation is a Mediterranean scrubland, and forest patches (mainly in mountainous areas) are dominated by Cork oak (Quercus suber) (13850 ha), Holm oak (Quercus ilex (1842 ha), Canary Island oak (Quercus canariensis) (1260 ha), and reforested at many sites by conifer plantations (11557 ha) (Ettaki et al 2019). Bioclimatically, it occupies the thermo-Mediterranean belt, with two main seasons: a hot and dry season between May and September, and a rainy and cold season from October to April (Elmoulat et al 2021). The average annual temperature is 22°C, with a range of 35°C-8°C (Ettaki et al 2019), and the average annual rainfall reaches 728 mm (Aboulaich et al 2013).…”

Landslide susceptibility mapping (LSM) of Swat District, Hindu Kush Himalayan region of Pakistan, using GIS-based bivariate modeling

“…WOE is a bivariate statistical model that uses the Bayesian rule to estimate probability, adopting the concepts of prior and posterior probability (Elmoulat et al, 2015). Researchers have used this method for mineral exploration mapping.…”

“…WOE is a GIS-based statistical model that estimates probability using the Bayesian principle and the concepts of prior and posterior probability. This technique is mostly applied in prediction mapping (Elmoulat et al, 2015;Hussain et al, 2022). The FR technique is considered to be a consistent and experimental method that can be used to calculate the association of landslide events with causative factors and to produce applicable LSM of the study area (Oh et al, 2017;Farhan et al, 2022).…”