Format

Send to

Choose Destination
Mar Pollut Bull. 2015 Sep 15;98(1-2):137-47. doi: 10.1016/j.marpolbul.2015.07.004. Epub 2015 Aug 12.

Parameter optimization method for the water quality dynamic model based on data-driven theory.

Author information

1
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China.
2
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China; Changjiang River Scientific Research Institute, Wuhan 430010, China.
3
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China. Electronic address: sunzc@dlut.edu.cn.

Abstract

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).

KEYWORDS:

Data-driven method; Function approximation; Parameter optimization; Water quality model

PMID:
26277602
DOI:
10.1016/j.marpolbul.2015.07.004
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center