National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com / submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
Rainfall is the most complex and difficult elements of hydrological cycle to understand and to model due to the complexity of atmospheric process. Long term prediction of rainfall is important for country like India where economy is mainly depends on agriculture. In the present study an attempt has been made to develop ANN models for prediction of daily rainfall for monsoon season at Parbhani District of Maharashtra, India. For the study, 30 years data (1985 to 2014) have been used. The 80% data (1985-2008) were used for model calibration and remaining 20% data (2008-2014) were used for validation. In the study, Gama test has been used to find best combination of input variables and after that back-propagation algorithm and tan sigmoid activation function were used to train and test the models. It was founded that the models are capable to predict the rainfall with adequate accuracy.
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