TY  - JOUR
T1  - Skill Scores Verification for all India Rainfall Data Using Artificial Neural Network
AU - Verma, Neeta AU - Arya, Y.D.S AU - Tripathi, K.C. 
JO  - International Journal of Soft Computing
VL  - 12
IS  - 1
SP  - 13
EP  - 19
PY  - 2017
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2017.13.19
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2017.13.19
KW  - Artificial Neural Network (ANN)
KW  -statistical parameters
KW  -backpropagation
KW  -skill scores parameters
KW  -India
AB  - There is great regional and temporal variation in the distribution of rainfall. The great variation in the
amount of rainfall both spatially and temporally, the high degree of uncertainty related to the date of arrival, etc.,
are unexplained. A neural network model for analyzing the all India rainfall has been developed by using the
142 years of data. In formulating artificial neural network based predictive models three layered network has
been constructed. The models under study are different in the number of hidden neurons. The main objective
of this study is to evaluate the applicability of ANN. The performance of different networks have been
evaluated and tested. The reason of using the ANN (Artificial Neural Network) model is based on prediction
by smartly analyzing the trend from the previously existing data set. In the present research, the last 142 years
data of all Indian rainfall has been analyzed through artificial neural network models. The Artificial Neural
Network (ANN) technique with back-propagation algorithm for the predictability of AIR with 1 lag by analysing
the historical time series of 142 years of AIR data. The ANN model used to forecast rainfall that is validated
using the correlation coefficient and Root Mean Square Error (RMSE). The statistical parameters are not
sufficient for model accuracy, so for accuracy skill scores for verification areobtained in this study.
ER  -