Consiglio Nazionale delle Ricerche (CNR-IGAG), Istituto di Geologia
Ambientale e Geoingegneria, Area della Ricerca di Roma 1, Via Salaria km
29.300, 00015 Monterotondo (Rome), Italy
Amerigo Mendicelli
Consiglio Nazionale delle Ricerche (CNR-IGAG), Istituto di Geologia
Ambientale e Geoingegneria, Area della Ricerca di Roma 1, Via Salaria km
29.300, 00015 Monterotondo (Rome), Italy
Consiglio Nazionale delle Ricerche (CNR-IGAG), Istituto di Geologia
Ambientale e Geoingegneria, Area della Ricerca di Roma 1, Via Salaria km
29.300, 00015 Monterotondo (Rome), Italy
Gianluca Acunzo
Consiglio Nazionale delle Ricerche (CNR-IGAG), Istituto di Geologia
Ambientale e Geoingegneria, Area della Ricerca di Roma 1, Via Salaria km
29.300, 00015 Monterotondo (Rome), Italy
Consiglio Nazionale delle Ricerche (CNR-IGAG), Istituto di Geologia
Ambientale e Geoingegneria, Area della Ricerca di Roma 1, Via Salaria km
29.300, 00015 Monterotondo (Rome), Italy
Giuseppe Naso
Presidenza del Consiglio dei Ministri, Dipartimento della Protezione
Civile (DPC), via Vitorchiano 2, 00189 Rome, Italy
Massimiliano Moscatelli
Consiglio Nazionale delle Ricerche (CNR-IGAG), Istituto di Geologia
Ambientale e Geoingegneria, Area della Ricerca di Roma 1, Via Salaria km
29.300, 00015 Monterotondo (Rome), Italy
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Total article views: 2,906 (including HTML, PDF, and XML)
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This work addresses the problem of the ground motion estimation over large areas as an important tool for seismic-risk reduction policies. In detail, the near-real-time estimation of ground motion is a key issue for emergency system management. Starting from this consideration, the present work proposes the application of a machine learning approach to produce ground motion maps, using nine input proxies. Such proxies consider seismological, geophysical, and morphological parameters.
This work addresses the problem of the ground motion estimation over large areas as an important...
