Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network

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Authors

Gabriele Amato , Matteo Fiorucci , Salvatore Martino , Luigi Lombardo , Lorenzo Palombi 

Abstract

The use of Artificial Neural Network (ANN) approaches has gained a significant role over the last decade in the field of predicting the distribution of effects triggered by natural forcing, this being particularly relevant for the development of adequate risk mitigation strategies. Among the most critical features of these approaches, there are the accurate geolocation of the available data as well as their numerosity and spatial distribution. The use of an ANN has never been tested at a national scale in Italy, especially in estimating earthquake-triggered landslides susceptibility. Based on the statistics deductible from the most up-to-date national inventory of earthquake-induced ground effects, i.e. the CEDIT catalogue, it results that over 56\% of the ground effects triggered by earthquakes in Italy are represented by landslides. Therefore, a landslide dataset with such high geolocation precision was suitable to evaluate the efficiency of an ANN to explain the distribution of landslides over the Italian territory. An ex-post evaluation of the ANN-based susceptibility model was also performed, using a sub-dataset of historical data with lower geolocation precision. The ANN training highly performed in terms of spatial prediction, by partitioning the Italian landscape into slope units.

The obtained results returned a distribution of potentially unstable slope units with maximum concentrations primarily distributed in the central-northern Apennines and secondarily in the southern Apennines. Moreover, the Alpine sector clearly appeared to be divided into two areas, a western one with relatively low susceptibility to earthquake-triggered landslides and the eastern sector with a higher susceptibility. However, the scale of the analysis carried out to train the ANN does not allow it to be applied for planning purposes or for seismic microzonation studies, for which training on a smaller spatial scale will be required.

DOI

https://doi.org/10.31223/X59W39

Subjects

Geomorphology

Keywords

Artificial Neural Network, Italy, Landslide susceptibility, CEDIT catalogue

Dates

Published: 2021-01-18 13:47

License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None.

Data Availability (Reason not available):
http://www.ceri.uniroma1.it/index_cedit.html

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