Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy

F. Falaschi (), F. Giacomelli, P. Federici, A. Puccinelli, G. D’Amato Avanzi, A. Pochini and A. Ribolini

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2009, vol. 50, issue 3, 569 pages

Abstract: This article presents a multidisciplinary approach to landslide susceptibility mapping by means of logistic regression, artificial neural network, and geographic information system (GIS) techniques. The methodology applied in ranking slope instability developed through statistical models (conditional analysis and logistic regression), and neural network application, in order to better understand the relationship between the geological/geomorphological landforms and processes and landslide occurrence, and to increase the performance of landslide susceptibility models. The proposed experimental study concerns with a wide research project, promoted by the Tuscany Region Administration and APAT-Italian Geological Survey, aimed at defining the landslide hazard in the area of the Sheet 250 “Castelnuovo di Garfagnana” (1:50,000 scale). The study area is located in the middle part of the Serchio River basin and is characterized by high landslide susceptibility due to its geological, geomorphological, and climatic features, among the most severe in Italy. Terrain susceptibility to slope failure has been approached by means of indirect-quantitative statistical methods and neural network software application. Experimental results from different methods and the potentials and pitfalls of this methodological approach have been presented and discussed. Applying multivariate statistical analyses made it possible a better understanding of the phenomena and quantification of the relationship between the instability factors and landslide occurrence. In particular, the application of a multilayer neural network, equipped for supervised learning and error control, has improved the performance of the model. Finally, a first attempt to evaluate the classification efficiency of the multivariate models has been performed by means of the receiver operating characteristic (ROC) curves analysis approach. Copyright Springer Science+Business Media B.V. 2009

Keywords: Landslide susceptibility; Logistic regression; Artificial neural network; GIS; ROC curves; Serchio River; Tuscany; Italy (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (13)

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DOI: 10.1007/s11069-009-9356-5

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