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.
Highlights
Water quality models play a very important role in water quality research that assesses ecosystem health and aids in the development of water pollution control strategies [1,2]
Models are often affected by noise in the distance operation can effectively reduce the influence of data noise on the calibration of a water quality data
This paper presents a novel algorithm (MCAD-Multi-objective particle swarm optimization (MOPSO)) to reduce the impact of noise on the model
Summary
Water quality models play a very important role in water quality research that assesses ecosystem health and aids in the development of water pollution control strategies [1,2]. Most water quality models are characterized by differential equations with multiple indicators and a large number of parameters [3,4]. These parameters may be difficult to obtain by experimental measurements or may not even have physical measurements. Model calibration can be manual or automatic. The manual trial-error process is usually inefficient, time consuming and labor intensive. It strongly depends on the experiential knowledge of the modeler. Implementation of this approach is becoming increasingly difficult due to
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