Please use this identifier to cite or link to this item: http://117.252.14.250:8080/jspui/handle/123456789/3203
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dc.contributor.authorSarkar, Archana-
dc.contributor.authorKumar, Rakesh-
dc.date.accessioned2019-07-26T09:41:55Z-
dc.date.available2019-07-26T09:41:55Z-
dc.date.issued2012-
dc.identifier.citationJournal of Water Resource and Protection, 2012, 4, 891-897en_US
dc.identifier.urihttp://117.252.14.250:8080/jspui/handle/123456789/3203-
dc.description.abstractThe Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input variables.en_US
dc.language.isoenen_US
dc.publisherScientific Research Publishingen_US
dc.subjectArtificial Neural Networks (ANNs)en_US
dc.subjectEvent Based Rainfall-Runoff Processen_US
dc.subjectError Back Propagationen_US
dc.subjectNeural Poweren_US
dc.titleArtificial Neural Networks for Event Based Rainfall-Runoff Modelingen_US
dc.typeArticleen_US
Appears in Collections:Research papers in International Journals

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