Assessing Landslide Susceptibility by Coupling Spatial Data Analysis and Logistic Model

Antonio Ganga, Mario Elia, Ersilia D’Ambrosio, Simona Tripaldi, Gian Franco Capra, Francesco Gentile and Giovanni Sanesi
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Antonio Ganga: Dipartimento di Architettura, Design e Urbanistica, Università degli Studi di Sassari, Viale Piandanna n 4, 07100 Sassari, Italy
Mario Elia: Department of Agricultural and Environmental Sciences, University of Bari Aldo Moro, 70126 Bari, Italy
Ersilia D’Ambrosio: Department of Agricultural and Environmental Sciences, University of Bari Aldo Moro, 70126 Bari, Italy
Simona Tripaldi: Department of Earth and Geo-Environmental Sciences, University of Bari Aldo Moro, 70121 Bari, Italy
Gian Franco Capra: Dipartimento di Architettura, Design e Urbanistica, Università degli Studi di Sassari, Viale Piandanna n 4, 07100 Sassari, Italy
Francesco Gentile: Department of Agricultural and Environmental Sciences, University of Bari Aldo Moro, 70126 Bari, Italy
Giovanni Sanesi: Department of Agricultural and Environmental Sciences, University of Bari Aldo Moro, 70126 Bari, Italy

Sustainability, 2022, vol. 14, issue 14, 1-13

Abstract: Landslides represent one of the most critical issues for landscape managers. They can cause injuries and loss of human life and damage properties and infrastructure. The spatial and temporal distribution of these detrimental events makes them almost unpredictable. Studies on landslide susceptibility assessment can significantly contribute to prioritizing critical risk zones. Further, landslide prevention and mitigation and the relative importance of the affecting drivers acquire even more significance in areas characterized by seismicity. This study aimed to investigate the relationship between a set of environmental variables and the occurrence of landslide events in an area of the Apulia Region (Italy). Logistic regression was applied to a landslide-prone area in the Apulia Region (Italy) to identify the main causative factors using a large dataset of environmental predictors (47). The results of this case study show that the logistic regression achieved a good performance, with an AUC (Area Under Curve) >70%. Therefore, the model developed would be a useful tool to define and assess areas for landslide occurrence and contribute to implementing risk mitigation strategy and land use policy.

Keywords: landslide; logistic regression; environmental hazard; risk assessment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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