DOI: 10.1016/j.marpolbul.2015.07.004
Scopus记录号: 2-s2.0-84941318073
论文题名: Parameter optimization method for the water quality dynamic model based on data-driven theory
作者: Liang S. ; Han S. ; Sun Z.
刊名: Marine Pollution Bulletin
ISSN: 0025-326X
EISSN: 1879-3363
出版年: 2015
卷: 98, 期: 2018-01-02 起始页码: 137
结束页码: 147
语种: 英语
英文关键词: Data-driven method
; Function approximation
; Parameter optimization
; Water quality model
Scopus关键词: Computation theory
; Dynamic models
; Particle swarm optimization (PSO)
; Sensitivity analysis
; Water quality
; Data-driven methods
; Function approximation
; Parameter optimization
; Parameter optimization methods
; Parameter sensitivity analysis
; Particle swarm optimization algorithm
; Water quality dynamics
; Water quality modeling
; Quality control
; algorithm
; environmental factor
; marine pollution
; optimization
; phytoplankton
; theoretical study
; three-dimensional modeling
; water quality
; Article
; artificial neural network
; China
; controlled study
; environmental factor
; environmental management
; environmental parameters
; learning algorithm
; machine learning
; mathematical computing
; nonhuman
; parameter optimization method
; Particle Swarm Optimization algorithm
; physical model
; phytoplankton
; process optimization
; sensitivity analysis
; simulation
; support vector machine
; water quality
; algorithm
; eutrophication
; theoretical model
; China
; Xiangshan Bay
; Zhejiang
; Algorithms
; China
; Eutrophication
; Models, Theoretical
; Phytoplankton
; Water Quality
Scopus学科分类: Agricultural and Biological Sciences: Aquatic Science
; Earth and Planetary Sciences: Oceanography
; Environmental Science: Pollution
英文摘要: 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). © 2015 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/85995
Appears in Collections: 过去全球变化的重建 全球变化的国际研究计划
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作者单位: State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, China; Changjiang River Scientific Research Institute, Wuhan, China
Recommended Citation:
Liang S.,Han S.,Sun Z.. Parameter optimization method for the water quality dynamic model based on data-driven theory[J]. Marine Pollution Bulletin,2015-01-01,98(2018-01-02)