Fuzzy Inference Systems For Synthetic Monthly Inflow Time Series Generation

dc.creatorLuna I.
dc.creatorBallini R.
dc.creatorSoares S.
dc.creatorDa Silva Filho D.
dc.date2011
dc.date2015-06-30T20:30:50Z
dc.date2015-11-26T14:50:34Z
dc.date2015-06-30T20:30:50Z
dc.date2015-11-26T14:50:34Z
dc.date.accessioned2018-03-28T22:01:46Z
dc.date.available2018-03-28T22:01:46Z
dc.identifier9789078677000
dc.identifierProceedings Of The 7th Conference Of The European Society For Fuzzy Logic And Technology, Eusflat 2011 And French Days On Fuzzy Logic And Applications, Lfa 2011. , v. 1, n. 1, p. 1060 - 1065, 2011.
dc.identifier
dc.identifier
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84871952300&partnerID=40&md5=6760a3b3908feca451f20d2e17c6be01
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/108175
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/108175
dc.identifier2-s2.0-84871952300
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1254178
dc.descriptionInflow data plays an important role in water and energy resources planning and management. In general, due to the limited availability of historical inflow data, synthetic streamflow time series have been widely used for several applications such as mid-and long-term hydropower scheduling and the identification of hydrological processes. This paper explores the use of fuzzy inference systems for the identification of two hydrological processes, and its use in the generation of synthetic monthly inflow sequences. Experiments using Brazilian monthly records show that fuzzy systems provide a promising approach for synthetic streamflow time series generation. © 2011. The authors-Published by Atlantis Press.
dc.description1
dc.description1
dc.description1060
dc.description1065
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dc.languageen
dc.publisher
dc.relationProceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2011 and French Days on Fuzzy Logic and Applications, LFA 2011
dc.rightsfechado
dc.sourceScopus
dc.titleFuzzy Inference Systems For Synthetic Monthly Inflow Time Series Generation
dc.typeActas de congresos


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