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Selection of genetic algorithm operators for river water quality model calibration

Ng, A. W. M and Perera, B. J. C (2003) Selection of genetic algorithm operators for river water quality model calibration. Engineering Applications of Artificial Intelligence, 16 (5-6). pp. 529-541. ISSN 0952-1976

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Abstract

Model calibration is the most important step in the overall model development. Genetic algorithm (GA) was used in this study to optimise model parameters of river water quality models. In general, the efficiency of using GA depends on the proper selection of GA operators, which are the components that make up the overall GA process. A comprehensive investigation on the importance of GA operators on model parameter optimisation was conducted in this study (and presented in this paper) based on the hypothesis that the selection of GA operators depends on the sensitivity of model parameters to model output. A numerical experiment on GA operators was conducted first considering a hypothetical river network water quality model with both insensitive and sensitive model parameters and were later validated using Yarra River Water Quality Model (YRWQM). It was found that a robust GA operator set obtained from the literature was capable of achieving a near-optimum model parameter set for the sensitive model but not for the insensitive model. However, due to insensitivity of water quality model parameters on model output, the ‘not so’ near-optimum parameter set did not contribute a great difference to the overall water quality predictions in the insensitive model. Therefore, based on these numerical experiments, it was concluded that a GA operator set obtained from the literature is adequate for calibration of model parameters of river water quality model using GA.

Item Type: Article
Uncontrolled Keywords: calibration, genetic algorithm, parameter optimisation, parameter sensitivity, water quality modelling, QUAL2E
Subjects: FOR Classification > 0502 Environmental Science and Management
FOR Classification > 0907 Environmental Engineering
Faculty/School/Research Centre/Department > College of Science and Engineering
Depositing User: VUIR
Date Deposited: 03 Jun 2014 00:22
Last Modified: 03 Jun 2014 01:36
URI: http://vuir.vu.edu.au/id/eprint/25258
DOI: https://doi.org/10.1016/j.engappai.2003.09.001
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Citations in Scopus: 50 - View on Scopus

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