The Algiers (north central Algeria) earthquake of August 1st, 2014 Mw 5.5 in the Algiers Bay tectonic context

Yelles‐Chaouche, Abdelkrim; Khelif, M. F.; Haned, A.; Chami, Adel; Aïdi, Chafik; Beldjoudi, H.; Kherroubi, A.; Benaïssa, Zahia

doi:10.1007/s10950-018-9806-1

Cited by 11 publications

(1 citation statement)

References 33 publications

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“…This seismic activity with an NW-SE compressional regime (Meghraoui 1988) results from the African and Eurasian tectonic plates convergence. Algiers is considered an unstable region marked by multiple deformations that generate moderate to strong earthquakes (Meghraoui 1991;Sebaï 1997;Yelles-Chaouche et al 1997;Bounif et al 2004;Bouhadad et al 2004;Meghraoui et al 2004;Yelles-Chaouche et al 2004;Harbi et al, 2004;Sebaï and Bernard 2008;Yelles-Chaouche et al 2018).…”

Section: General Settingmentioning

confidence: 99%

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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