Please use this identifier to cite or link to this item: http://117.252.14.250:8080/jspui/handle/123456789/6864
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDesai, V. R.-
dc.contributor.authorMishra, A. K.-
dc.date.accessioned2022-06-15T21:23:34Z-
dc.date.available2022-06-15T21:23:34Z-
dc.date.issued2006-
dc.identifier.urihttp://117.252.14.250:8080/jspui/handle/123456789/6864-
dc.description.abstractDrought forecasting plays an important role in the planning and management of natural resources including water resource-systems of a river basin, since drought has severe effect when it persists over a longer period Modeling and forecasting of droughts, which are nonlinear and non-stationary, is a complex exercise. During the last decade neural networks have shown great ability in modeling and forecasting nonlinear and non- stationary time series. In this study an application of the back propagation feed forward recursive ANN models are presented to forecast droughts., The models were applied to forecast droughts using standardized precipitation index series_as 'drought indices in the Kansabati River Basin, which lies in the Purulia District of West Bengal. The resulting trained network is capable of forecasting with satisfactory results upto 2-months of lead time. The model can be used for water resource management in the river basinen_US
dc.language.isoenen_US
dc.publisherNational Institute of Hydrologyen_US
dc.subjectKansabati River Basinen_US
dc.subjectNeural networksen_US
dc.subjectStandardized precipitation indexen_US
dc.subjectDrought indicesen_US
dc.title5-Drought Forecasting Using Standardized Precipitation Index.en_US
dc.typeTechnical Reporten_US
Appears in Collections:26-Jal Vigyan Sameeksha Vol.-21(1-2)-2006

Files in This Item:
File Description SizeFormat 
5-Drought Forecasting Using Standardized Precipitation Index..pdf18.51 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.