Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/45446
Title: Development of hydrolprocess framework for rainfall-runoff modeling in the river Brahmaputra basin
Authors: Mishra, Satanand
Saravanan, C.
Dwivedi, V. K.
Shukla, J. P.
Keywords: Feed Forward Backpropagation Algorithm;Multilayer Perceptron;Artificial Neural Network;Supervised Learning;Unsupervised Learning;Error tolerance Factor
Issue Date: Dec-2018
Publisher: NISCAIR-CSIR, India
Abstract: The 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.
Page(s): 2369-2381
URI: http://nopr.niscair.res.in/handle/123456789/45446
ISSN: 0975-1033 (Online); 0379-5136 (Print)
Appears in Collections:IJMS Vol.47(12) [December 2018]

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