POSSIBILITY of RESERVOIR COMPUTING in DAM INFLOW PREDICTION

Abstract

Our country also increases many practical application examples of deep neural networks. Many research and application examples exist in the social capital infrastructure field. On the other side, we cannot improve to prepare the big data, which is indispensable for deep Learning. Unlike data under certain indoor conditions such as factories, data related to social capital infrastructure outdoors is diverse, and it is expected that database development that expresses that diversity will progress in the future. In this paper, based on such a situation, we applied reservoir computing, which has fewer parameters than deep neural networks, to dam inflow prediction and confirmed its practicality. Although it is possible to secure a certain degree of prediction accuracy, the accuracy is inferior to that of deep neural networks. It was found that it is necessary to devise networks in the future.