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

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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.

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Data Availability

Landslide data can be found in the CEDIT catalogue, available at the link https://gdb.ceri.uniroma1.it/index.php/view/map/?repository=cedit&project=Cedit (as mentioned in the text)

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Acknowledgements

Authors acknowledge the project "Distribution analysis of earthquake-induced instability effects based on a national scale inventory for the probabilistic definition of multi-hazard scenarios" (University of Rome Sapienza - RM120172A2582F52 - year 2020) supervised by Prof. Salvatore Martino.

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Authors and Affiliations

  1. “Sapienza” University of Rome, Department of Earth Sciences and Research Centre for Geological Risks (CERI), P. le A. Moro 5, 00185, Rome, Italy

    Gabriele Amato, Matteo Fiorucci & Salvatore Martino

  2. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, PO Box 217, 7500, Enschede, AE, Netherlands

    Luigi Lombardo

  3. “Nello Carrara” Applied Physics Institute - National Research Council of Italy (IFAC-CNR), Via Madonna del Piano 10, 50019, Sesto Fiorentino (FI), Italy

    Lorenzo Palombi

  4. University of Cassino and Southern Lazio, Department of Civil and Mechanical Engineering, Cassino, Italy

    Matteo Fiorucci

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Correspondence to Gabriele Amato.

Appendix. Predictors’ information

Appendix. Predictors’ information

Table 2 List of the predictors assigned to each slope unit. Codes reported in “Predictors code and description” have been used to represent the results of the permutation feature importance. Predictors have been grouped as indicated in “Group” to perform the combination-group analysis. Terrain characteristics were calculated from a 20-m Digital Elevation Model (DEM)
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Table 3 Description of the classes of the categorical predictor “Lithology”
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Table 4 Description of the reference soil groups compared in the classes of the categorical predictor “Soil type”
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Amato, G., Fiorucci, M., Martino, S. et al. Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network. Bull Eng Geol Environ 82, 160 (2023). https://doi.org/10.1007/s10064-023-03163-x

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