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Water 2018, 10(1), 32; https://doi.org/10.3390/w10010032

Parameter Estimation of Water Quality Models Using an Improved Multi-Objective Particle Swarm Optimization

1,2
,
1,2,* and 1,2,3,†,*
1
Key Laboratory of Integrated Regulation and Resource Development on Shallow Lake of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China
2
National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China
3
School of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 22500, China
All the authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 28 October 2017 / Revised: 17 December 2017 / Accepted: 29 December 2017 / Published: 3 January 2018
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Abstract

Water quality models are of great importance for developing policies to control water pollution, with the model parameters playing a decisive role in the simulation results. It is necessary to introduce estimation through multi-objective parameters, which is often affected by noise in the data, into water quality models. This paper presents a multi-objective particle swarm optimization algorithm, which is based on the Mahalanobis distance operation, mechanism of cardinality preference and advection-diffusion operator. The Mahalanobis distance operation can effectively reduce the influence of noise in the data on model calibration. The mechanism of cardinality preference and the use of the advection-diffusion operator can prevent non-dominated solutions from falling into the local optimum. Four cases were used to test the proposed approach. The first two cases with true Pareto fronts show that this approach can accurately estimate the true Pareto front with a good distribution, even in the presence of noise. Furthermore, the application of the approach was tested by the O’Connor model and Crops of Engineers Integrated Compartment Water Quality Model. We show that our approach can produce satisfactory results for the multi-objective calibration of complex water quality models. In general, the proposed approach can provide accurate and efficient parameter estimation in water quality models. View Full-Text
Keywords: water quality model; improved multi-objective particle swarm optimization; parameter estimation; anti-noise performance water quality model; improved multi-objective particle swarm optimization; parameter estimation; anti-noise performance
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Wang, Y.; Hua, Z.; Wang, L. Parameter Estimation of Water Quality Models Using an Improved Multi-Objective Particle Swarm Optimization. Water 2018, 10, 32.

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