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Application of GIS-based statistical modeling for landslide susceptibility mapping in the city of Azazga, Northern Algeria

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Abstract

Landslide susceptibility mapping is a necessary tool in order to manage the landslides hazard and improve the risk mitigation. In this research, we validate and compare the landslide susceptibility maps (LSMs) produced by applying four geographic information system (GIS)-based statistical approaches including frequency ratio (FR), statistical index (SI), weights of evidence (WoE), and logistic regression (LR) for the urban area of Azazga. For this purpose, firstly, a landslide inventory map was prepared from aerial photographs and high-resolution satellite imagery interpretation, and detailed fieldwork. Seventy percent of the mapped landslides were selected for landslide susceptibility modeling, and the remaining (30%) were used for model validation. Secondly, ten landslide factors including the slope, aspect, altitude, land use, lithology, precipitation, distance to drainage, distance to faults, distance to lineaments, and distance to roads have been derived from high-resolution Alsat 2A satellite images, aerial photographs, geological map, DEM, and rainfall database. Thirdly, we established LSMs by evaluating the relationships between the detected landslide locations and the ten landslides factors using FR, SI, LR, and WoE models in GIS. Finally, the obtained LSMs of the four models have been validated using the receiver operating characteristics curves (ROCs). The validation process indicated that the FR method provided more accurate prediction (78.4%) in generating LSMs than the SI (78.1%),WoE (73.5%), and LR (72.1%) models. The results revealed also that all the used statistical models provided good accuracy in landslide susceptibility mapping.

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Acknowledgements

This research was supported by the National Center of Applied Research in Earthquake Engineering (CGS). The authors would like to express their sincere appreciation and gratitude to the reviewers and the Editor-in-Chief for their valuable comments, suggestions and corrections for improving the original manuscript. The authors would like also to thank the Algerian Space Agency (ASAL) for providing Alsat2A satellite imagery and the National Hydrous Resources (ANRH) for providing rainfall data. Thanks are also due to the DUAC (Direction de l’Urbanisme, de l’Aménagementet de la Construction) of Tizi-Ouzou for providing various datasets needed in this research.

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  1. Division Microzonage Sismique, Centre National de Recherche Appliquée en Génie Parasismique (CGS), 1 Rue Kaddour Rahim prolongée, Hussein Dey, Algeria

    Hamid Bourenane, Aghiles Abdelghani Meziani & Dalila Ait Benamar

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  1. Hamid Bourenane
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  2. Aghiles Abdelghani Meziani
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Correspondence to Hamid Bourenane.

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Bourenane, H., Meziani, A.A. & Benamar, D.A. Application of GIS-based statistical modeling for landslide susceptibility mapping in the city of Azazga, Northern Algeria. Bull Eng Geol Environ 80, 7333–7359 (2021). https://doi.org/10.1007/s10064-021-02386-0

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