期刊
HYDROLOGY RESEARCH
卷 48, 期 5, 页码 1214-1225出版社
IWA PUBLISHING
DOI: 10.2166/nh.2016.149
关键词
back propagation neural network; multiple linear regression; particle swarm optimization algorithm; support vector machine; water quality parameters
资金
- Chinese Academy for Environmental Planning
- Tianjin Normal University Doctor Foundation [52XB1517]
- innovation team training plan of the Tianjin Education Committee [TD12-5037]
- National Natural Science Foundation of China [41372373]
Dissolved oxygen (DO) is an important indicator reflecting the healthy state of aquatic ecosystems. The balance between oxygen supply and consuming in the water body is significantly influenced by physical and chemical parameters. This study aimed to evaluate and compare the performance of multiple linear regression (MLR), back propagation neural network (BPNN), and support vector machine (SVM) for the prediction of DO concentration based on multiple water quality parameters. The data set included 969 samples collected from rivers in China and the 16 predicted variables involved physical factors, nutrients, organic substances, and metal ions, which would affect the DO concentrations directly or indirectly by influencing the water-air exchange, the growth of water plants, and the lives of aquatic animals. The models optimized by particle swarm optimization (PSO) algorithm were calibrated and tested, with nearly 80% and 20% data, respectively. The results showed that the PSO-BPNN and PSO-SVM had better predicted performances than linear regression methods. All of the evaluated criteria, including coefficient of determination, mean squared error, and absolute relative errors suggested that the PSO-SVM model was superior to the MLR and PSO-BPNN for DO prediction in the rivers of China with limited knowledge of other information.
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