Mostrar el registro sencillo del ítem
dc.contributor.author | Rao, Zhengfu | es_ES |
dc.contributor.author | Alvarruiz Bermejo, Fernando | es_ES |
dc.date.accessioned | 2014-11-10T12:08:57Z | |
dc.date.issued | 2007-01 | |
dc.identifier.issn | 1464-7141 | |
dc.identifier.uri | http://hdl.handle.net/10251/44016 | |
dc.description | "The definitive peer-reviewed and edited version of this article is published in Journal of Hydroinformatics , vol.. 9, n. 1[ (15-27) 2007. DOI: 10.2166/hydro.2006.01 and is available at www.iwapublishing.com.” | es_ES |
dc.description.abstract | [EN] As part of the POWADIMA research project, this paper describes the technique used to predict the consequences of different control settings on the performance of the water-distribution network, in the context of real-time, near-optimal control. Since the use of a complex hydraulic simulation model is somewhat impractical for real-time operations as a result of the computational burden it imposes, the approach adopted has been to capture its domain knowledge in a far more efficient form by means of an artificial neural network (ANN). The way this is achieved is to run the hydraulic simulation model off-line, with a large number of different combinations of initial tank-storage levels, demands, pump and valve settings, to predict future tank-storage water levels, hydrostatic pressures and flow rates at critical points throughout the network. These input/output data sets are used to train an ANN, which is then verified using testing sets. Thereafter, the ANN is employed in preference to the hydraulic simulation model within the optimization process. For experimental purposes, this technique was initially applied to a small, hypothetical water-distribution network, using EPANET as the hydraulic simulation package. The application to two real networks is described in subsequent papers of this series. | es_ES |
dc.description.sponsorship | The POWADIMA research project was funded by the European Commission under its Vth Framework thematic programme on Energy, Environment and Sustainable Development (Contract Number EVK1-CT-2000-00084). The authors would like to take this opportunity to thank the Commission and project officers for their support throughout the duration of the contract. | |
dc.language | Inglés | es_ES |
dc.publisher | IWA Publishing | es_ES |
dc.relation.ispartof | Journal of Hydroinformatics | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Artificial neural network | es_ES |
dc.subject | Hydraulic simulation model | es_ES |
dc.subject | POWADIMA | es_ES |
dc.subject | Replication | es_ES |
dc.subject | Water | es_ES |
dc.subject.classification | CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | Use of an artificial neural network to capture the domain knowledge of a conventional hydraulic simulation model | es_ES |
dc.type | Artículo | es_ES |
dc.embargo.lift | 10000-01-01 | |
dc.identifier.doi | 10.2166/hydro.2006.014 | |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/FP5/EVK1-CT-2000-00084/EU/Potable water distribution management/POWADIMA/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.description.bibliographicCitation | Rao, Z.; Alvarruiz Bermejo, F. (2007). Use of an artificial neural network to capture the domain knowledge of a conventional hydraulic simulation model. Journal of Hydroinformatics. 9(1):15-24. doi:10.2166/hydro.2006.014 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://www.iwaponline.com/jh/009/jh0090015.htm | es_ES |
dc.description.upvformatpinicio | 15 | es_ES |
dc.description.upvformatpfin | 24 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 9 | es_ES |
dc.description.issue | 1 | es_ES |
dc.relation.senia | 30172 | |
dc.contributor.funder | European Commission |