Machine Learning for Prompt Estimation of Macroseismic Intensity from Seismometric Data in Italy

Authors: L. Patelli, M. Cameletti, V. De Rubeis, N. A. Pino, C. Piromallo, P. Sbarra, P. Tosi

This repository contains the functions and the data used to produce the results presented in the paper "Machine Learning for Prompt Estimation of Macroseismic Intensity from Seismometric Data in Italy" (Patelli et al., 2025).

The following R packages are required to run the codes:

• randomForest >= 4.7-1.2 (link)
• iml >= 0.11.4 (link)
• tidyverse >= 2.0.0 (link)
• sf >= 1.0-20 (link)
• xtable >= 1.8-4 (link)

Main References

• Luca Patelli, Michela Cameletti, Valerio De Rubeis et al. Machine Learning for Prompt Estimation of Macroseismic Intensity from Seismometric Data in Italy, 14 October 2025, PREPRINT (Version 1) available at Research Square (link).
• Breiman, L., 2001. Random Forests. Machine Learning 45, 5-32 (link).
• Molnar, C. (2025). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (3rd ed.) (link).