Abstract
Too low a concentration of dissolved oxygen (DO) in a river can disrupt the ecological balance, while too high a concentration may lead to eutrophication of the water body and threaten the health of the aquatic environment. Therefore, accurate prediction of DO concentration is crucial for water resource protection. In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). Firstly, DWT-db4 was used to denoise the noisy water quality feature data; secondly, the meteorological data were simplified into four principal components by KPCA; finally, the water quality features and meteorological principal components were inputted into the GWO-optimized XGBoost model as features for training and prediction. The prediction performance of the model was comprehensively assessed by comparison with other machine learning models using MAE, MSE, MAPE, NSE, KGE and WI evaluation metrics. The model was tested at three different locations and the results showed that the model outperformed the other models, performing as follows: 0.5925, 0.6482, 6.3322, 0.8523, 0.8902, 0.9403; 0.4933, 0.4325, 6.2351, 0.8952, 0.7928, 0.8632; 0.2912, 0.2001, 4.0523, 0.7823, 0.8425, 0.8463 and the PICP values exceed 95%. The hybrid model demonstrated significant results in predicting dissolved oxygen concentrations for the next 15 days. Compared with other studies, we innovatively improved the prediction accuracy of the model significantly through noise removal and the introduction of multi-source features.