Please use this identifier to cite or link to this item: http://117.252.14.250:8080/jspui/handle/123456789/3048
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dc.contributor.authorLohani, A. K.-
dc.contributor.authorKumar, Rakesh-
dc.contributor.authorSingh, R. D.-
dc.date.accessioned2019-07-05T10:26:42Z-
dc.date.available2019-07-05T10:26:42Z-
dc.date.issued2008-
dc.identifier.urihttp://117.252.14.250:8080/jspui/handle/123456789/3048-
dc.description.abstractArtificial neural network (ANN) is an efficient and useful technique and gaining popularity in hydrological modeling and forecasting. This paper presents the application of ANNs to hydrologic time series modeling, and is illustrated by an application to model the monthly reservoir inflow of Gandhi Sagar reservoir. The advantage of the ANN method is that it does not require the artificial neural network model structure to be known a priori, in contrast to most of the time series modeling techniques. The results showed that the ANN forecasted reservoir inflow series preserves the statistical properties of the original inflow series. The model also showed good performance in terms of various statistical indices. The results are highly promising.en_US
dc.language.isoenen_US
dc.publisherNational Institute of Hydrologyen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectTime series modelling of reservoir iInflowsen_US
dc.title44-Time series modelling of reservoir iInflows through back Propagation Artificial Neural Networken_US
dc.typeTechnical Reporten_US
Appears in Collections:Proceedings of the National Seminar on Conservation and Restoration of lakes (CAROL-08), 16-17 October 2008 at Nagpur, Volume - II



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