Importance of Genetic Algorithm Operators in River Water Quality Model Parameter Optimisation

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Ng, A. W. M and Perera, B. J. C (2001) Importance of Genetic Algorithm Operators in River Water Quality Model Parameter Optimisation. In: MODSIM 2001: International Congress on Modelling and Simulation, the Australian National University, Canberra, Australia, 10-13 December 2001: integrating models for natural resources management across disciplines, issues and scales: proceedings. Ghassemi, Fereidoun, ed. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Canberra, ACT, pp. 1943-1948.


Other book title: Proceedings of the International Congress on Modelling and Simulation (MODSIM 2001), Canberra, Australia, 10-13 December 2001. Well-calibrated river water quality models are required to assess the effectiveness of various management strategies, which are aimed at improving river water quality. Model calibration (or parameter estimation) is an important part of overall model development. A river water quality model was developed for Yarra River in Victoria (Australia) and was calibrated using a genetic algorithm (GA). In general, the efficiency of GA depends on the proper selection of GA operators, which prompted an investigation of these operators in achieving the 'optimum' model parameter set for the Yarra River water quality model. This was conducted by considering a hypothetical river network water quality model with both insensitive and sensitive reaction parameters and later verified by the Yarra River water quality model. Based on limited numerical experiments, it was found that GA with a reasonable operator set obtained from literature was capable of achieving a near-optimum model parameter set in river water quality models. However, it is recommended that further studies be conducted to verify the above findings.

Item type Book Section
Official URL
ISBN 0867405252
Subjects Historical > FOR Classification > 0905 Civil Engineering
Current > Division/Research > College of Science and Engineering
Keywords water quality modelling, genetic algorithm, parameter optimisation, QUAL2E, calibration
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