EXAMINATION OF SHORT-TERM WATER LEVEL PREDICTION MODEL IN LOW-LYING LAKES USING MACHINE LEARNING

Abstract

 Targeting the Lake Toyanogata basin in the Kamedago district of Niigata City, which is located in a low-lying agricultural area, we developed a machine learning model that outputs the inflow to the Lake Toyanogata by inputting rainfall data and drainage data from the Lake Toyanogata. The applicability to a short-term water level prediction model was then examined. For the input learning data of the machine learning model, we attempted to use the artificially generated mock data acquired by using the drainage analysis simulation as well as the observed data in the past rainfall event. As a result, it became possible to perform a simulation in a short time even for a big rainfall event that was not included in the measured data, suggesting the effectiveness of the method of complementing the learning data with the artificially generated data. In addition, it was shown that the proposed short-term water level prediction model could be applied to a tool that supports the decision of the operation of drainage pump stations.