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|Title:||Development of hydrolprocess framework for rainfall-runoff modeling in the river Brahmaputra basin|
Dwivedi, V. K.
Shukla, J. P.
|Keywords:||Feed Forward Backpropagation Algorithm;Multilayer Perceptron;Artificial Neural Network;Supervised Learning;Unsupervised Learning;Error tolerance Factor|
|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.|
|ISSN:||0975-1033 (Online); 0379-5136 (Print)|
|Appears in Collections:||IJMS Vol.47(12) [December 2018]|
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|IJMS 47(12) 2369-2381.pdf||350.32 kB||Adobe PDF||View/Open|
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