Prediction of salinity concentration in Hichirippu-numa through long short-term memory using data assimilation

抄録

In the aquaculture industry, damage occurs because of a sudden decrease in salinity concentration. Therefore, the demand for real-time forecasting has increased. Forecasting through machine learning is increasing; however, observation stations at the target site are not always present. Therefore, we predicted the flow field at the target site through data assimilation (DA) using a method combining the Kalman filter and finite element method. In this study, we used the predicted values with DA for long short-term memory and improved the prediction accuracy.