Enhancing artificial neural networks applied to the optimal design of water distribution systems

Manuel A. Andrade, Christopher Y. Choi, Mario R. Mondaca, Kevin Lansey, Doosun Kang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationWorld Environmental and Water Resources Congress 2013
Subtitle of host publicationShowcasing the Future - Proceedings of the 2013 Congress
Pages648-662
Number of pages15
StatePublished - Nov 18 2013
EventWorld Environmental and Water Resources Congress 2013: Showcasing the Future - Cincinnati, OH, United States
Duration: May 19 2013May 23 2013

Publication series

NameWorld Environmental and Water Resources Congress 2013: Showcasing the Future - Proceedings of the 2013 Congress

Other

OtherWorld Environmental and Water Resources Congress 2013: Showcasing the Future
CountryUnited States
CityCincinnati, OH
Period5/19/135/23/13

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ASJC Scopus subject areas

  • Water Science and Technology

Cite this

Andrade, M. A., Choi, C. Y., Mondaca, M. R., Lansey, K., & Kang, D. (2013). Enhancing artificial neural networks applied to the optimal design of water distribution systems. In World Environmental and Water Resources Congress 2013: Showcasing the Future - Proceedings of the 2013 Congress (pp. 648-662). (World Environmental and Water Resources Congress 2013: Showcasing the Future - Proceedings of the 2013 Congress).