A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China

• Published in: Environmental monitoring and assessment Vol. 193; no. 6; p. 363
• Main Authors: Song, Chenguang, Yao, Leihua, Hua, Chengya, Ni, Qihang
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2021; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Autocorrelation; Back propagation networks; China; Decomposition; Discharge measurement; Dissolved oxygen; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental Monitoring; Environmental science; Forecasting; Gaging stations; Heuristic methods; Least squares; Least-Squares Analysis; Mathematical models; Model accuracy; Monitoring/Environmental Analysis; Neural networks; Noise reduction; Nonlinear systems; Nonlinearity; Optimization; Oxygen; Parameters; Passeriformes; prediction; Prediction models; Rivers; Search algorithms; Stability; Stream discharge; Support Vector Machine; Support vector machines; Water Quality; Water resources; Water resources management; Yangtze River; China; Yangtze River; SSA; Optimization algorithm; Hybrid model; Water quality prediction; VMD; LSSVM
• ISSN: 0167-6369; 1573-2959; 1573-2959
• DOI: 10.1007/s10661-021-09127-6

Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in a river system. The accuracy of traditional water quality prediction methods is generally low, and the prediction results have serious autocorrelation. To overcome nonstationarity, randomness, and nonlinearity of the water quality parameter data, an improved least squares support vector machine (LSSVM) model was proposed to improve the model’s performance at two gaging stations, namely Panzhihua and Jiujiang, in the Yangtze River, China. In addition, a hybrid model that recruits variational mode decomposition (VMD) to denoise the input data was adopted. A novel metaheuristic optimization algorithm, the sparrow search algorithm (SSA) was also implemented to compute the optimal parameter values for the LSSVM model. To validate the proposed hybrid model, standalone LSSVM, SSA-LSSVM, VMD-LSSVM, support vector regression (SVR), as well as back propagation neural network (BPNN) were considered as the benchmark models. The results indicated that the VMD-SSA-LSSVM model exhibited the best forecasting performance among all the peer models at Panzhihua station. Furthermore, the model forecasting results applied at Jiujiang were consistent with those at Panzhihua station. This result further verified the accuracy and stability of the VMD-SSA-LSSVM model. Thus, the proposed hybrid model was effective method for forecasting nonstationary and nonlinear water quality parameter series and can be recommended as a promising model for water quality parameter forecasting.