LSTM model to predict missing data of dissolved oxygen in land‐based aquaculture farm

A long short‐term memory (LSTM) model is introduced to predict missing datapoints of dissolved oxygen (DO) in an eel ( Anguilla japonica ) recirculating aquaculture system. Field experiments allow to determine periodic patterns in DO data corresponding to day–night cycles and a DO decrease after feeding. To improve the accuracy of DO prediction by using a training‐to‐test data ratio of 5:1, training with data in sequential and reverse orders is performed and evaluated. The LSTM model used to predict DO levels in the fish tank has an error of approximately 3.25%. The proposed LSTM model trained on DO data has a high applicability and may support water quality control in aquaculture farms.