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Using genetic algorithms to calibrate a water quality model.

Research output: Contribution to journalJournal article

Published

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Using genetic algorithms to calibrate a water quality model. / Liu, Shuming; Butler, David; Brazier, Richard E.; Heathwaite, A. Louise; Khu, Soon-Thiam.

In: Science of the Total Environment, Vol. 374, No. 2-3, 15.03.2007, p. 260-272.

Research output: Contribution to journalJournal article

Harvard

Liu, S, Butler, D, Brazier, RE, Heathwaite, AL & Khu, S-T 2007, 'Using genetic algorithms to calibrate a water quality model.', Science of the Total Environment, vol. 374, no. 2-3, pp. 260-272. https://doi.org/10.1016/j.scitotenv.2006.12.042

APA

Liu, S., Butler, D., Brazier, R. E., Heathwaite, A. L., & Khu, S-T. (2007). Using genetic algorithms to calibrate a water quality model. Science of the Total Environment, 374(2-3), 260-272. https://doi.org/10.1016/j.scitotenv.2006.12.042

Vancouver

Liu S, Butler D, Brazier RE, Heathwaite AL, Khu S-T. Using genetic algorithms to calibrate a water quality model. Science of the Total Environment. 2007 Mar 15;374(2-3):260-272. https://doi.org/10.1016/j.scitotenv.2006.12.042

Author

Liu, Shuming ; Butler, David ; Brazier, Richard E. ; Heathwaite, A. Louise ; Khu, Soon-Thiam. / Using genetic algorithms to calibrate a water quality model. In: Science of the Total Environment. 2007 ; Vol. 374, No. 2-3. pp. 260-272.

Bibtex

@article{d070c7b7a8994643980f9421348760d9,
title = "Using genetic algorithms to calibrate a water quality model.",
abstract = "With the increasing concern over the impact of diffuse pollution on water bodies, many diffuse pollution models have been developed in the last two decades. A common obstacle in using such models is how to determine the values of the model parameters. This is especially true when a model has a large number of parameters, which makes a full range of calibration expensive in terms of computing time. Compared with conventional optimisation approaches, soft computing techniques often have a faster convergence speed and are more efficient for global optimum searches. This paper presents an attempt to calibrate a diffuse pollution model using a genetic algorithm (GA). Designed to simulate the export of phosphorus from diffuse sources (agricultural land) and point sources (human), the Phosphorus Indicators Tool (PIT) version 1.1, on which this paper is based, consisted of 78 parameters. Previous studies have indicated the difficulty of full range model calibration due to the number of parameters involved. In this paper, a GA was employed to carry out the model calibration in which all parameters were involved. A sensitivity analysis was also performed to investigate the impact of operators in the GA on its effectiveness in optimum searching. The calibration yielded satisfactory results and required reasonable computing time. The application of the PIT model to the Windrush catchment with optimum parameter values was demonstrated. The annual P loss was predicted as 4.4 kg P/ha/yr, which showed a good fitness to the observed value.",
keywords = "Diffuse pollution, Genetic algorithm, Model calibration, Phosphorus Indicators Tool",
author = "Shuming Liu and David Butler and Brazier, {Richard E.} and Heathwaite, {A. Louise} and Soon-Thiam Khu",
year = "2007",
month = mar,
day = "15",
doi = "10.1016/j.scitotenv.2006.12.042",
language = "English",
volume = "374",
pages = "260--272",
journal = "Science of the Total Environment",
issn = "0048-9697",
publisher = "Elsevier Science B.V.",
number = "2-3",

}

RIS

TY - JOUR

T1 - Using genetic algorithms to calibrate a water quality model.

AU - Liu, Shuming

AU - Butler, David

AU - Brazier, Richard E.

AU - Heathwaite, A. Louise

AU - Khu, Soon-Thiam

PY - 2007/3/15

Y1 - 2007/3/15

N2 - With the increasing concern over the impact of diffuse pollution on water bodies, many diffuse pollution models have been developed in the last two decades. A common obstacle in using such models is how to determine the values of the model parameters. This is especially true when a model has a large number of parameters, which makes a full range of calibration expensive in terms of computing time. Compared with conventional optimisation approaches, soft computing techniques often have a faster convergence speed and are more efficient for global optimum searches. This paper presents an attempt to calibrate a diffuse pollution model using a genetic algorithm (GA). Designed to simulate the export of phosphorus from diffuse sources (agricultural land) and point sources (human), the Phosphorus Indicators Tool (PIT) version 1.1, on which this paper is based, consisted of 78 parameters. Previous studies have indicated the difficulty of full range model calibration due to the number of parameters involved. In this paper, a GA was employed to carry out the model calibration in which all parameters were involved. A sensitivity analysis was also performed to investigate the impact of operators in the GA on its effectiveness in optimum searching. The calibration yielded satisfactory results and required reasonable computing time. The application of the PIT model to the Windrush catchment with optimum parameter values was demonstrated. The annual P loss was predicted as 4.4 kg P/ha/yr, which showed a good fitness to the observed value.

AB - With the increasing concern over the impact of diffuse pollution on water bodies, many diffuse pollution models have been developed in the last two decades. A common obstacle in using such models is how to determine the values of the model parameters. This is especially true when a model has a large number of parameters, which makes a full range of calibration expensive in terms of computing time. Compared with conventional optimisation approaches, soft computing techniques often have a faster convergence speed and are more efficient for global optimum searches. This paper presents an attempt to calibrate a diffuse pollution model using a genetic algorithm (GA). Designed to simulate the export of phosphorus from diffuse sources (agricultural land) and point sources (human), the Phosphorus Indicators Tool (PIT) version 1.1, on which this paper is based, consisted of 78 parameters. Previous studies have indicated the difficulty of full range model calibration due to the number of parameters involved. In this paper, a GA was employed to carry out the model calibration in which all parameters were involved. A sensitivity analysis was also performed to investigate the impact of operators in the GA on its effectiveness in optimum searching. The calibration yielded satisfactory results and required reasonable computing time. The application of the PIT model to the Windrush catchment with optimum parameter values was demonstrated. The annual P loss was predicted as 4.4 kg P/ha/yr, which showed a good fitness to the observed value.

KW - Diffuse pollution

KW - Genetic algorithm

KW - Model calibration

KW - Phosphorus Indicators Tool

U2 - 10.1016/j.scitotenv.2006.12.042

DO - 10.1016/j.scitotenv.2006.12.042

M3 - Journal article

VL - 374

SP - 260

EP - 272

JO - Science of the Total Environment

JF - Science of the Total Environment

SN - 0048-9697

IS - 2-3

ER -