Chapter
Jul 8, 2013
Enhancing Artificial Neural Networks Applied to the Optimal Design of Water Distribution Systems
Authors: Manuel A. Andrade, Christopher Y. Choi, Mario R. Mondaca, Kevin Lansey, and Doosun KangAuthor Affiliations
Publication: World Environmental and Water Resources Congress 2013: Showcasing the Future
Abstract
Achieving an optimal design for a typical water distribution system (WDS) essentially involves determining which combination of pipes and arrangements will produce the most efficient and economical network. Solving the problem is a complex process, one well suited to computationally intensive heuristic methods. Including water quality constraints can pose a special challenge due to the demanding, extended-period simulations involved. Employing artificial neural networks (ANNs) can reduce the amount of computation time needed. ANNs can in fact approximate disinfectant concentrations in a fraction of the time required by a conventional water quality model. This study presents a methodology for improving the accuracy of ANNs applied to the optimal design of a WDS by means of a probabilistic approach based on the fast finding of a network similar to the optimal WDS. This work also presents a methodology to find such a network. ANNs trained with the probabilistic dataset generated using the proposed approach were shown to be more accurate than their counterparts trained with a random dataset.
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© 2013 American Society of Civil Engineers.
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Published online: Jul 8, 2013
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Authors
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Manuel A. Andrade
Department of Agricultural and Biosystems Engineering, The University of Arizona, Tucson, AZ 85721
Christopher Y. Choi
Department of Biological Systems Engineering, University of Wisconsin, Madison, WI 53706
Mario R. Mondaca
Department of Biological Systems Engineering, University of Wisconsin, Madison, WI 53706
Kevin Lansey
Department of Civil Engineering and Engineering Mechanics, The University of Arizona, Tucson, AZ 85721
Doosun Kang
Deptartment of Civil Engineering, The University of Suwon, 445-743, San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, South Korea
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ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.
Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.