DSpace Repository

44-Time series modelling of reservoir iInflows through back Propagation Artificial Neural Network

Show simple item record

dc.contributor.author Lohani, A. K.
dc.contributor.author Kumar, Rakesh
dc.contributor.author Singh, R. D.
dc.date.accessioned 2019-07-05T10:26:42Z
dc.date.available 2019-07-05T10:26:42Z
dc.date.issued 2008
dc.identifier.uri http://117.252.14.250:8080/jspui/handle/123456789/3048
dc.description.abstract Artificial 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.iso en en_US
dc.publisher National Institute of Hydrology en_US
dc.subject Artificial Neural Networks en_US
dc.subject Time series modelling of reservoir iInflows en_US
dc.title 44-Time series modelling of reservoir iInflows through back Propagation Artificial Neural Network en_US
dc.type Technical Report en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account