Dhar, A., and Datta, B. (2006) Chance constrained water quality management model for reservoir systems. ISH Journal of Hydraulic Engineering, 12 (3). pp. 39-48.

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Abstract

Operation of reservoir systems to comply with water quality criteria is becoming an important management goal. A methodology is developed for optimal short term operation of reservoir systems to control the downstream water quality, using Genetic Algorithm based linked simulation optimization. The single objective management model minimizes the deviation of storage at the end of a management time step from the specified target storage, while maintaining the downstream flow requirements. The optimization model incorporates the uncertainties in prediction of inflows to the reservoir. The cumulative distribution funcions of inflows are incorporated as an approximated distribution function in the optimization model and are used to define chance constraints with associated reliabilities. Performance of the developed GA based linked simulation optimization model is evaluated for an illustrative application. Performance evaluation resutls show that the developed methodology is capable of evolving optimal release strategies to satisfy water quality requirements downstream of the reservoir.

Item ID:	9347
Item Type:	Article (Research - C1)
ISSN:	0971-5010
Keywords:	reservoir operation; chance constraint; water quality management; genetic algorithm
Related URLs:	http://www.e-ish.net/PUBLICATIONS.HTM
Date Deposited:	16 Jun 2010 04:09
FoR Codes:	09 ENGINEERING > 0905 Civil Engineering > 090509 Water Resources Engineering @ 50% 09 ENGINEERING > 0907 Environmental Engineering > 090702 Environmental Engineering Modelling @ 50%
SEO Codes:	96 ENVIRONMENT > 9606 Environmental and Natural Resource Evaluation > 960604 Environmental Management Systems @ 50% 96 ENVIRONMENT > 9609 Land and Water Management > 960999 Land and Water Management of Environments not elsewhere classified @ 25% 96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960506 Ecosystem Assessment and Management of Fresh, Ground and Surface Water Environments @ 25%
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