Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU)

Abstract

Dissolved oxygen (DO), an important water quality indicator in aquaculture, affects the survival rate of aquatic creatures and the yield of aquatic production. Therefore, it is important to predict DO in fishery ponds for applying artificial aeration with low energy and cost. Recently, deep learning models, such as recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), are often used to predict the trend of time series, but it is unclear which one of them is more suitable for prediction of DO in fishery ponds. In this work, the RNN model, LSTM model, and GRU model were used to build three DO predicting models. The performance of the three models were compared by mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and the coefficient of determination (R2). The performance of RNN is worse result than LSTM and GRU. The four evaluation indicators of GRU are 0.450 mg/L, 0.411, 0.054, and 0.994, and the four indicators of LSTM are 0.407 mg/L, 0.294, 0.059, and 0.970, which shows that the performance of GRU is similar to LSTM, but the time cost and number of parameters used for GRU is much lower than LSTM. It is concluded that the GRU has overall better performance and can be applied to practical applications.