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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2019)
[Effective from January 1, 2019]
For more details click here

ICV 2017: 100.00
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2017: 100.00
For more details click here

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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
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Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2017: 100.00
NAAS RATING 2018: 5.38

Int.J.Curr.Microbiol.App.Sci.2019.8(5): 1328-1334
DOI: https://doi.org/10.20546/ijcmas.2019.805.151


Rainfall-Runoff Prediction based on Artificial Neural Network: A Case Study Priyadarshini Watershed
S.K. Kothe1, B.L. Ayare1*, H.N. Bhange1 and S.T. Patil2
1Department of Soil & Water Conservation Engineering,
2Department of Irrigation & Drainage Engineering, CAET, DBSKKV, Dapoli, Maharashtra, India-415712
*Corresponding author
Abstract:

Hydrological modelling is a powerful technique of hydrologic system investigation for both the research hydrologists and the practicing water resources engineers involved in the planning and development of integrated approach for management of water resources. In present study, the observed rainfall and runoff data of 2010, 2011, 2013 and 2014years were used as input data. In ANN, input data was divided in 70 per cent, 15 per cent and 15 per cent for training, testing and validation purpose, respectively. Rainfall-runoff models play an important role in water resource management planning and therefore, 70 numbers of different types of models with various degrees of complexity have been developed for this purpose. The output from ANN was tested with statistical parameters, viz. root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2) and correlation coefficient (r). The rainfall-runoff relationship is one of the most complex hydrologic phenomena and it is based on tremendous spatial and temporal variability of watershed characteristics, precipitation patterns, etc. Therefore other models were not performing well. The ANN model 1-48-1 architecture was selected as the best. The comparisons between the measured and predicted values of runoff showed that the ANN model could be successfully applied and provide high accuracy and reliability for estimation of runoff from un-gauged watershed with rainfall as input parameter.


Keywords: ANN, Modelling, Runoff Prediction, Statistical performance, Watershed
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How to cite this article:

Kothe, S.K., B.L. Ayare, H.N. Bhange and Patil, S.T. 2019. Rainfall-Runoff Prediction based on Artificial Neural Network: A Case Study Priyadarshini Watershed.Int.J.Curr.Microbiol.App.Sci. 8(5): 1328-1334. doi: https://doi.org/10.20546/ijcmas.2019.805.151