Dissolved Oxygen Prediction Model for the Yangtze River Estuary Basin Using IPSO-LSSVM

• Published in: Water (Basel) Vol. 15; no. 12; p. 2206
• Main Authors: Li, Yongguo, Li, Xiangyan, Xu, Caiyin, Tang, Xuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2023
• Subjects: Algorithms; Ammonia; Analysis; Artificial intelligence; combined model; Correlation coefficient; Correlation coefficients; Dissolved oxygen; dissolved oxygen prediction; Environmental protection; Estuaries; Eutrophication; IPSO; Lagrange multiplier; LSSVM; Machine learning; Model accuracy; Neural networks; Nitrogen; Optimization; Phosphorus; Potassium; Potassium permanganate; Prediction models; Principal components analysis; River basins; Rivers; Root-mean-square errors; Support vector machines; Technical services; Turbidity; Water quality; Water temperature; China; Yangtze River
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w15122206

Water ecology has always been key to environmental protection, and the combination of human activities and natural factors has caused eutrophication in the Yangtze estuary and adjacent waters. Among them, dissolved oxygen (DO) concentration is the key indicator to judge the quality of water. Firstly, using principal component analysis (PCA) to determine the number of parameters affecting dissolved oxygen concentration, the least squares support vector machine (LSSVM) prediction model with improved particle swarm optimization (IPSO) is proposed to be applied to the dissolved oxygen prediction in Shanghai’s Yangtze River basin through the data-driven modeling approach and the regression prediction capability of the neural network. Eight parameters of water temperature (WT), pH, potassium permanganate (KMnO4), ammonia nitrogen (NH4+-N), total phosphorus (TP), total nitrogen (TN), conductivity (Cond), and nephelometric turbidity unit (NTU) are selected as model inputs in the published public data, and the output is the dissolved oxygen concentration. The optimal combination of model parameters is found according to the IPSO algorithm, which effectively overcomes the parameter selection problem of regular support vector machines (SVM). The mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficients of the evaluation indexes of this model (R2) are 0.1702, 0.2221, 0.0267, and 0.9751, respectively. Compared with other similar data driven models, this model has improved model accuracy and stability in predicting DO concentrations in the estuary, and thus it provides technical support for assessing and monitoring offshore water quality.