PURPOSE:To automatically and precisely predict (the time change of) rain peak flow-in quantity for supporting a pump operation. CONSTITUTION:A neural network-applicated rain flow-in quantity predicting device is characterized in such a way that integrated rain quantity data which are obtained by calculating an observed value from a radar rain gauge 1 in terms of mesh-shaped rainfall strength and which are discretized by an equal time interval from the start of rainfall is stored as input data for learning, rain flow-in quantity which are obtained by operating rain flow-in quantity being made to flow in a pump equipment from the water level meter 2 of the pump equipment and from discharge quantity by the pump operation and which are discretized by the equal time interval from the start of rainfall is stored as output data for learning, the weight coefficient of a neural network is learned and stored by a back propagation method based on integrated rain quantity data being input data for learning and rain flow-in quantity being output data for learning and rain flow-in quantity is predicted and outputted by the neural network 48 from the integrated rain quantity data and judged value of the day when it rains by using the weight coefficient.
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