[A-CG43] 地球環境科学と人工知能/機械学習

[ACG43-P03] LSTMを用いた平成30年7月豪雨時の鹿児島市における降水量の再現

*白澤 元気¹、濵﨑 琉生¹、加古 真一郎²、中村 啓彦³ (1.鹿児島大学　工学部、2.鹿児島大学大学院理工学研究科、3.鹿児島大学　水産学部)

キーワード：LSTM、集中豪雨、レーダーアメダス、アメダス、降水予測

In this study, we constructed a long short-term memory (LSTM) model to hindcast the torrential rainfall event that occurred in Kagoshima City in July 2018. In this model, automated meteorological data acquisition system (AMeDAS) and radar–AMeDAS rainfall data from Kyushu were used as training data. Our LSTM model could hindcast the time variation of the rainfall event; however, it tended to underestimate the rainfall event observed by AMeDAS, and in some cases the start times of the precipitation events were late. Underestimation and phase lags were probably because the LSTM model was optimized for light rainfall events that occur frequently. To further improve the prediction accuracy of our model, it was necessary to improve the reproducibility of the magnitude of the precipitation amplitude. Therefore, we selected further training data such as physical quantities over the East China Sea that contribute to the forecast/hindcast of torrential rainfall events. The use of radar–AMeDAS rainfall data as training data over the ocean in the southern part of Kagoshima significantly improved the accuracy of our LSTM model compared to that without this additional training data.