Spatial distribution, controlling factors and failure mechanisms of the large-scale landslides in the urban area of Azazga city (northern Algeria)

Bourenane, Hamid; Braham, Massinissa; Bouhadad, Youcef; Meziani, Aghiles Abdelghani

doi:10.1007/s12665-021-09607-5

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…In northern Algeria, landslides are among the most damaging geohazards that threaten mountainous regions, hilly terrain, agricultural fields, and even urban areas, causing significant economic losses, material damage, and human casualties (Machane et al 2008;Djerbal and Melbouci 2012;Bougdal et al 2013;Hadji et al 2013;Bourenane et al 2014;Bourenane et al 2016;Achour et al 2017;Cheikh Lounis et al 2017;Hallal et al 2017;Hadji et al 2017;Meziani et al 2017;Mahdadi et al 2018;Merghadi et al 2018;Hallal et al 2019;Mezzereg et al 2019;Bourenane et al 2021aBourenane et al et 2021bGoumrasa et al 2021;Tebbouche et al 2022).…”

Section: Introductionmentioning

confidence: 99%

“…However, research on landslide susceptibility mapping is still scarce and rather insufficient as it covers limited areas in northern Algeria (Hadji et al 2013;Bourenane et al 2014;Bourenane et al 2016;Hadji et al 2017;Djerbal et al 2017;Mahdadi et al 2018;Bourenane et al 2019;Karim et al 2019;Merghadi et al 2020;Bourenane et al 2021aBourenane et al et 2021bGoumrasa et al 2021;Benbouras 2022;Bounemeur et al 2022).…”

Section: Introductionmentioning

confidence: 99%

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Integration of multi-criteria decision analysis and statistical models for landslide susceptibility mapping in the western Algiers province (Algeria) using GIS techniques and remote sensing data

MOKADEM

Lounis

Machane³

et al. 2023

Preprint

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Landslide susceptibility assessment and prediction are among the main processing for disaster management and land use planning activities. Therefore, the general purpose of this research was to evaluate GIS-based spatial modeling of landslides in the western Algiers province using five models, namely: frequency ratio (FR), weights of evidence (WoE), evidential belief function (EBF), logistic regression (LR) and analytical hierarchy process (AHP), then, compare their performances. At first, a landslide inventory map was prepared according to Google Earth satellite images, historical records, and extensive field surveys. The recorded landslides were divided into two groups (70% and 30%) to establish the training and validation models. In the next step, GIS techniques and remote sensing data were used, to prepare a spatial database containing thirteen landslide conditioning factors; lithology, distance to lithological boundaries, permeability, slope, exposure, altitude, profile curvature, plan curvature, precipitation, distance to rivers, TWI, NDVI, and distance to roads. Finally, the landslide susceptibility maps were produced using the five models and validated by the areas under the relative operative characteristic curve (AUC). The AUC results showed a significant improvement in susceptibility map accuracy, the FR model has the best performance in the training and prediction process (90%), followed by LR (88%, 89%), WoE (88%, 87%), EBF (86%,86%), and AHP (76%,75%), respectively. The produced maps in the current study could be useful for land use planning and hazard mitigation purposes in western Algiers province.

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