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GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province, N-E Algeria

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Abstract

Slope instability phenomena in Souk Ahras region are annually causing a great amount of damage mainly to road infrastructure, water main supply, and buildings. The main problem is that instabilities keep reoccurring despite the remedial measures brought about every time. The fact is there is not only a single factor that is behind these instabilities rather than the interplay of a large variety of factors pertaining to the geological, geomorphological, and hydrological characteristics of the terrain as well as human-related activities. Consequently, a spatial database of ten landslide-related factors were identified and used to assess landslide susceptibility and establish a model capable of predicting landslide prone areas. For this reason, three statistical methods are used for the landslide susceptibility assessment: logistic regression, frequency ratio, and weights of evidence in a GIS platform. A landslide inventory map was established from visual interpretation of satellite images and field survey data. Three landslide susceptibility maps were produced using different statistical models. Each susceptibility map subdivides the study area into five classes of landslide susceptibility: very low, low, moderate, high, and very high. These raster-based susceptibility maps were compared and verified with both training and validating inventory data. The area under the curve values, based on success rate, are between 82.11 and 90.57%, and those based on prediction rate are between 83.14 and 90.91%. The results showed that the logistic regression method is more consistent and reliable than the two other techniques, and it has the best performance among the three statistical methods.

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Acknowledgements

The authors would like to thank Dr. Debi Prasanna Kanungo and the anonymous reviewers for their valuable comments of the manuscript. The data analysis was carried out as a part of the first author’s PhD studies at the Department of Geology, Earth Sciences, Geography and Spatial Planning Faculty, University of Constantine 1, Constantine, Algeria.

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Authors and Affiliations

  1. Department of Geology Sciences, University of Frères Mentouri-Constantine 1, 25000, Constantine, Algeria

    Fatna Mahdadi

  2. Department of Geology, Sciences Faculty, Tebessa University, Tebessa, Algeria

    Abederrahmane Boumezbeur

  3. Department of Earth Sciences, Institute of Architecture and Earth Sciences, Sétif University, Setif, Algeria

    Riheb Hadji & Farid Zahri

  4. Geotechnical Engineering Group, CSIR-Central Building Research Institute, Roorkee, 247667, India

    Debi Prasanna Kanungo

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  1. Fatna Mahdadi
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  2. Abederrahmane Boumezbeur
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  4. Debi Prasanna Kanungo
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  5. Farid Zahri
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Correspondence to Fatna Mahdadi.

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Mahdadi, F., Boumezbeur, A., Hadji, R. et al. GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province, N-E Algeria. Arab J Geosci 11, 476 (2018). https://doi.org/10.1007/s12517-018-3770-5

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