Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/45446
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dc.contributor.authorMishra, Satanand-
dc.contributor.authorSaravanan, C.-
dc.contributor.authorDwivedi, V. K.-
dc.contributor.authorShukla, J. P.-
dc.date.accessioned2018-11-28T09:19:07Z-
dc.date.available2018-11-28T09:19:07Z-
dc.date.issued2018-12-
dc.identifier.issn0975-1033 (Online); 0379-5136 (Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/45446-
dc.description2369-2381en_US
dc.description.abstractThe developed new Hydrolprocess is a combination of clustering, regression analysis and Artificial Neural Network (ANN) which gives the complete result of data analysis, discovering pattern, and prediction of hydrological parameters for the catchment. Hydrological parameters such as rainfall, river water level, discharge, temperature, evaporation, and sediment has been observed with respect to time. Monthly rainfall and runoff data from 1990 to 2010 of Brahmaputra river basin has been taken for the classification, clustering and development of the ANN model. Developed ANN models have been able to predict runoff with great accuracy. Performance of the model on the basis of correlation coefficient (R), root mean-square error (RMSE), and percentage error have been computedas0.98, 4.5 and 3.5 respectively.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceIJMS Vol.47(12) [December 2018]en_US
dc.subjectFeed Forward Backpropagation Algorithmen_US
dc.subjectMultilayer Perceptronen_US
dc.subjectArtificial Neural Networken_US
dc.subjectSupervised Learningen_US
dc.subjectUnsupervised Learningen_US
dc.subjectError tolerance Factoren_US
dc.titleDevelopment of hydrolprocess framework for rainfall-runoff modeling in the river Brahmaputra basinen_US
dc.typeArticleen_US
Appears in Collections:IJMS Vol.47(12) [December 2018]

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