Estimating landslide susceptibility through a artificial neural network classifier

Paraskevas Tsangaratos () and Andreas Benardos ()

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2014, vol. 74, issue 3, 1489-1516

Abstract: A landslide susceptibility analysis is performed through an artificial neural network (ANN) algorithm, in order to model the nonlinear relationship between landslide manifestation and geological and geomorphological parameters. The proposed methodology can be divided into two distinctive phases. In the first phase, the methodology introduces a specific distance metric, the Mahalanobis distance metric, to improve the selection of non-landslide records that “enriches” the training database and provides the model with the necessary data during the training phase. In the second phase, the methodology develops a ANN model that was capable of minimizing the effect of over-fitting by monitoring in parallel the testing data during the training phase and terminating the process of learning when a certain acceptable criteria are achieved. The model was capable in identifying unstable areas, expressed by a landslide susceptibility index. The proposed methodology has been applied in the County of Xanthi, in the northern part of Greece, an area where a well-established landslide database existed. The landslide-related parameters that had been taken in account in the analysis were the following: lithology, distance from geological boundaries, distance from tectonic features, elevation, slope inclination, slope orientation, distance from hydrographic network and distance from road network. These parameters have been normalized and reclassified and used as input variables, while the description of a given area as landslide/non-landslide was assumed to be the output variable. The final outcome of the model was a geospatial product, which expressed the landslide susceptibility index and when compared with an up-to-date landslide inventory database showed satisfactory results. Copyright Springer Science+Business Media Dordrecht 2014

Keywords: Landslide susceptibility; Mahalanobis metric distance; Artificial neural network (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s11069-014-1245-x

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