Precipitation as the meteorological data is closely related to human life. For this reason, we hope to propose new method to forecast it more accurately. In this article, we aim to forecast precipitation by reservoir computing with some additional processes. The concept of reservoir computing emerged from a specific machine learning paradigm, which is characterized by a three-layered architecture (input, reservoir and output layers). What is different from other machine learning algorithms is that only the output layer is trained and optimized for particular tasks. Since the precipitation data is non-smooth, its prediction is very difficult via the classical methods of prediction of the nonlinear time series. For the predicated precipitation data, we take its first-order moving average to make it smoother, then take the logarithm of smoothed nonzero data and the same negative constant for smoothed zero data to obtain a new series. We train the obtained series by reservoir computing and get the predicated result of its future. After taking its exponent function, the predicated data for original precipitation data are obtained. It indicates that reservoir computing combined with other processes can potentially bring about the accurate precipitation forecast.
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