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

Manuel A. Andrade, Christopher Y. Choi, Mario R. Mondaca, Kevin E 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: Showcasing the Future - Proceedings of the 2013 Congress
Pages648-662
Number of pages15
StatePublished - 2013
EventWorld Environmental and Water Resources Congress 2013: Showcasing the Future - Cincinnati, OH, United States
Duration: May 19 2013May 23 2013

Other

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

Fingerprint

artificial neural network
water quality
methodology
heuristics
pipe
water distribution system
simulation

ASJC Scopus subject areas

  • Water Science and Technology

Cite this

Andrade, M. A., Choi, C. Y., Mondaca, M. R., Lansey, K. E., & 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)

Enhancing artificial neural networks applied to the optimal design of water distribution systems. / Andrade, Manuel A.; Choi, Christopher Y.; Mondaca, Mario R.; Lansey, Kevin E; Kang, Doosun.

World Environmental and Water Resources Congress 2013: Showcasing the Future - Proceedings of the 2013 Congress. 2013. p. 648-662.

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

Andrade, MA, Choi, CY, Mondaca, MR, Lansey, KE & 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, Cincinnati, OH, United States, 5/19/13.
Andrade MA, Choi CY, Mondaca MR, Lansey KE, Kang D. 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. 2013. p. 648-662
Andrade, Manuel A. ; Choi, Christopher Y. ; Mondaca, Mario R. ; Lansey, Kevin E ; Kang, Doosun. / Enhancing artificial neural networks applied to the optimal design of water distribution systems. World Environmental and Water Resources Congress 2013: Showcasing the Future - Proceedings of the 2013 Congress. 2013. pp. 648-662
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