A Deep Learning based architecture for rainfall estimation integrating heterogeneous data sources

Rain gauges are sensors providing direct measurement of precipitation intensity at individual point sites, and, usually, spatial interpolation methods are used to obtain an estimate of the precipitation field over the entire area of interest. Among them, Kriging with External Drift (KED) is a largely used and well-recognized method in this field. However, interpolation methods need to work with real-time data, and therefore can be hardly used in real-time scenarios. To overcome this issue, we propose a general machine learning framework, which can be trained offline, based on a deep learning architecture, also integrating information derived from remote sensing measurements such as weather radars and satellites. The framework allows to provide accurate estimations of the rainfall in the areas where no rain gauge data is available. Experimental results, conducted on real data concerning a southern region in Italy, provided by the Department of Civil Protection (DCP), show significant improvement in comparison with KED and other machine learning techniques.