Research on Short-Term Multi-Step Prediction of River Dissolved Oxygen based on STL-LSTM

In order to improve the multi-step prediction accuracy of dissolved oxygen in rivers, this paper proposes a multi-step prediction model for dissolved oxygen in rivers based on the combination of STL-LSTM and multi input multi output strategy (STL-MIMO-LSTM). Firstly, the dissolved oxygen is decomposed into trend, seasonal, and residual components using the Seasonal and Trend decomposition using Loess (STL) method to enhance data features. Then, the model is constructed using the multi-input multi-output strategy (MIMO) combined with the Long Short-Term Memory (LSTM) model; Finally, the LSTM model was applied to the prediction experiment of dissolved oxygen in the Beijing-Hangzhou Grand Canal. The experimental results show that the mean absolute error (MAE) of the STL-MOMO-LSTM model in the next 4 steps is 0.07, 0.1024, 0.1211, 0.1319, and the mean square error (MSE) is 0.0134, 0.0288, 0.0399, 0.0467. Except for the first time step, the prediction accuracy is better than the recursive and direct multi-step prediction models. The MSE prediction accuracy decay rates of STL-MIMO-LSTM for he 2nd, 3rd, and 4th time steps are 1.1496%, 0.3878%, and 0.1688%, respectively, which are lower than other models for the 2nd and 3rd-time steps, while slightly higher than the STL-DIRECT-LSTM model (0.1501%) for 4th-time steps. Therefore, the model exhibits high prediction accuracy and stable prediction performance, can effectively predict the future trend of dissolved oxygen, and provide references for river water quality management.