### 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 language | English (US) |
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Title of host publication | World Environmental and Water Resources Congress 2013: Showcasing the Future - Proceedings of the 2013 Congress |

Pages | 648-662 |

Number of pages | 15 |

State | Published - 2013 |

Event | World Environmental and Water Resources Congress 2013: Showcasing the Future - Cincinnati, OH, United States Duration: May 19 2013 → May 23 2013 |

### Other

Other | World Environmental and Water Resources Congress 2013: Showcasing the Future |
---|---|

Country | United States |

City | Cincinnati, OH |

Period | 5/19/13 → 5/23/13 |

### Fingerprint

### ASJC Scopus subject areas

- Water Science and Technology

### Cite this

*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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*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.

}

TY - GEN

T1 - Enhancing artificial neural networks applied to the optimal design of water distribution systems

AU - Andrade, Manuel A.

AU - Choi, Christopher Y.

AU - Mondaca, Mario R.

AU - Lansey, Kevin E

AU - Kang, Doosun

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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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UR - http://www.scopus.com/inward/citedby.url?scp=84887451434&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84887451434

SN - 9780784412947

SP - 648

EP - 662

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

ER -