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An application of artificial neural networks for rainfall forecasting
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In previous work, we have proposed a constructive methodology for temporal data learning supported by results and prescriptions related to the embedding theorem, and using the singular spectrum analysis both in order tso reduce the effects of the possible discontinuity of the signal and to implement an efficient ensemble method. In this paper we present new results concerning the application of this approach to the forecasting of the individual rain-fall intensities series collected by 135 stations distributed in the Tiber basin. The average RMS error of the obtained forecasting is less than 3 mm of rain.