A hybrid XGBoost-ISSA-LSTM model for accurate short-term and long-term dissolved oxygen prediction in ponds

Dissolved oxygen (DO) is one of the most critical factors to measure the water quality in ponds, which greatly impacts on healthy growth of aquatic organisms. To improve the prediction accuracy of DO and grasp its changing trends, a novel hybrid DO prediction model based on the long short-term memory network (LSTM) optimized by an improved sparrow search algorithm (ISSA) is proposed. Firstly, to discard redundant information and improve the calculation speed of the model, the key factors that have a greater correlation with DO are selected as the input parameters by extreme gradient boosting (XGBoost). Secondly, towards expanding the searching range of sparrows and balancing the global and local search, we introduce an adaptive factor exponential declining strategy for producers, and an arcsine decreasing strategy for scouters, which nonlinearly decreases with the increase of iterations. Besides, we also improve the position updating of scouters, making the sparrows gradually move to the best position. Finally, LSTM is optimized by ISSA to get the best initial weights and thresholds to construct an XGBoost-ISSA-LSTM DO prediction model. Specifically, we first analyze the method for water quality prediction, which can make short-term prediction (including about 1 h, 2 h) and long-term prediction (including about 12 h, 24 h) of DO. In 1-h prediction, the root mean square error (RMSE) of the model is 0.5571, the mean absolute error (MAE) is 0.2572, and the R² is 0.9276. In 24 h prediction, RMSE of the model is 0.6310, MAE is 0.4562, and R² is 0.9082. The experimental results show that the proposed model has better generalization performance and higher prediction accuracy compared with other common models. Therefore, the presented model based on XGBoost-ISSA-LSTM is more effective and could meet the actual demand of accurate prediction of DO.