Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/487498
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dc.coverage.spatialCivil Engineering
dc.date.accessioned2023-05-31T09:32:48Z-
dc.date.available2023-05-31T09:32:48Z-
dc.identifier.urihttp://hdl.handle.net/10603/487498-
dc.description.abstractWater resources related studies involve variables which are highly random and uncertain in nature Most hydrological variables exhibit a high degree of temporal and spatial variability These studies are very essential to the mankind for providing a warning of the extreme flood or drought conditions and help to optimize the operation of systems like reservoirs and power plants etc For better hydrological design we need proper modelling of the system using these variables Many approaches were
dc.format.extentNot Available
dc.languageEnglish
dc.relationNot Available
dc.rightsself
dc.titleFuzzy neural network modelling for hydrological studies
dc.title.alternativeNot available
dc.creator.researcherDeka, Paresh Chandra
dc.subject.keywordEngineering
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering Civil
dc.description.noteNot Available
dc.contributor.guideChandramouli, V and Dutta, Anjan
dc.publisher.placeGuwahati
dc.publisher.universityIndian Institute of Technology Guwahati
dc.publisher.institutionDEPARTMENT OF CIVIL ENGINEERING
dc.date.registered1999
dc.date.completed2003
dc.date.awarded2003
dc.format.dimensionsNot Available
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:DEPARTMENT OF CIVIL ENGINEERING

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