Parameter optimization method for the water quality dynamic model based on data-driven theory.
Parameter optimization is important for developing a water quality dynamic model. In this study, we applied data-driven method to select and optimize parameters for a complex three-dimensional water quality model. First, a data-driven model was developed to train the response relationship between phytoplankton and environmental factors based on the measured data. Second, an eight-variable water quality dynamic model was established and coupled to a physical model. Parameter sensitivity analysis was investigated by changing parameter values individually in an assigned range. The above results served as guidelines for the control parameter selection and the simulated result verification. Finally, using the data-driven model to approximate the computational water quality model, we employed the Particle Swarm Optimization (PSO) algorithm to optimize the control parameters. The optimization routines and results were analyzed and discussed based on the establishment of the water quality model in Xiangshan Bay (XSB).
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China.
Shuxiu
S
Liang
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China; Changjiang River Scientific Research Institute, Wuhan 430010, China.
Songlin
S
Han
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China. Electronic address: sunzc@dlut.edu.cn.
Zhaochen
Z
Sun
D000465:Algorithms; D002681:China; D005068:Eutrophication; D008962:Models, Theoretical; D010839:Phytoplankton; D060753:Water Quality
Data-driven method; Function approximation; Parameter optimization; Water quality model