Accurate rainfall estimates are critical for areas presenting high hydrological risks. We have devised a general machine learning framework based on a deep learning architecture, which also integrates information derived from remote sensing measurements, such as weather radars and satellites. Experimental results conducted on real data from a southern region in Italy, provided by the Department of Civil Protection (DCP), show significant improvements compared to current state-of-the-art methods.
Using Deep Learning and Data Integration for Accurate Rainfall Estimates
Gianluigi Folino;Massimo Guarascio;Francesco Chiaravalloti;
2020
Abstract
Accurate rainfall estimates are critical for areas presenting high hydrological risks. We have devised a general machine learning framework based on a deep learning architecture, which also integrates information derived from remote sensing measurements, such as weather radars and satellites. Experimental results conducted on real data from a southern region in Italy, provided by the Department of Civil Protection (DCP), show significant improvements compared to current state-of-the-art methods.File in questo prodotto:
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