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

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
20 Downloads (Pure)

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. The CEDIT catalogue, the most up-to-date national inventory of earthquake-induced ground effects, was adopted 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 Apennines and secondarily in the southern and northern 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 higher susceptibility. Our work clearly demonstrates that if funds for risk mitigation were allocated only on the basis of rainfall-induced landslide distribution, large areas highly susceptible to earthquake-triggered landslides would be completely ignored by mitigation plans.

Original languageEnglish
Article number160
JournalBulletin of engineering geology and the environment
Volume82
Issue number5
DOIs
Publication statusPublished - May 2023

Keywords

  • Artificial Neural Network
  • CEDIT catalogue
  • Italy
  • Landslide susceptibility
  • Slope unit partition
  • 2024 OA procedure
  • ITC-HYBRID
  • ITC-ISI-JOURNAL-ARTICLE

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