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pro vyhledávání: '"Thaisa Goulart"'
Autor:
Bruno Melo Brentan, Thaisa Goulart, Daniel Manzi, Edevar Luvizotto Junior, Gustavo Meirelles Lima
Publikováno v:
Revista DAE, Vol 66, Iss 212, Pp 16-31 (2018)
O dimensionamento ótimo de redes de distribuição de água permite a concepção e a instalação dessas utilidades, que são o principal ativo das companhias de saneamento, com menores custos de implantação e atendendo a restrições operacionai
Publikováno v:
Water Resources Management. 31:4339-4351
The success of hydraulic simulation models of water distribution networks is associated with the ability of these models to represent real systems accurately. To achieve this, the calibration phase is essential. Current calibration methods are based
Autor:
Bruno Melo Brentan, Daniel Manzi, Enrique Campbell, Gustavo Meirelles, Manuel Herrera, Joaquín Izquierdo, Edevar Luvizotto, Thaisa Goulart
Publikováno v:
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname
Brentan, B M, Campbell, E, Goulart, T, Manzi, D, Meirelles, G, Herrera, M, Izquierdo, J & Luvizotto Jr., E 2018, ' Social network community detection and hybrid optimization for dividing water supply into district metered areas ', Journal of Water Resources Planning and Management, vol. 144, no. 5, 04018020 . https://doi.org/10.1061/(ASCE)WR.1943-5452.0000924
instname
Brentan, B M, Campbell, E, Goulart, T, Manzi, D, Meirelles, G, Herrera, M, Izquierdo, J & Luvizotto Jr., E 2018, ' Social network community detection and hybrid optimization for dividing water supply into district metered areas ', Journal of Water Resources Planning and Management, vol. 144, no. 5, 04018020 . https://doi.org/10.1061/(ASCE)WR.1943-5452.0000924
Water supply utilities need to properly manage their systems to guarantee a quality supply. One way to manage large systems is through division into district metered areas (DMAs). Graph clustering with an unknown number of subdivisions, as in social
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88231738e82c7150e7075962e0807948