Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley,Italy |
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Authors: | F Falaschi F Giacomelli P R Federici A Puccinelli G D’Amato Avanzi A Pochini A Ribolini |
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Institution: | (1) Pisa University, Via S. Maria 53, 56126 Pisa, Italy;(2) Gamma Informatica, Lucca, Italy |
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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. |
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Keywords: | Landslide susceptibility Logistic regression Artificial neural network GIS ROC curves Serchio River Tuscany Italy |
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