TY  - JOUR
T1  - Comutational Performance of GRNN in Weather Forecasting
AU - Santhanam, Tiruvenkadam AU - Subhajini, A.C. 
JO  - Asian Journal of Information Technology
VL  - 10
IS  - 5
SP  - 165
EP  - 169
PY  - 2011
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2011.165.169
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2011.165.169
KW  - Multilayer perception
KW  -weather forecasting
KW  -rainfall prediction
KW  -Radial Basis Function (RBF)
KW  -back propagation
KW  -Artificial Neural Network (ANN)
KW  -Numerical Weather Prediction (NWP)
KW  -Regression Neural Networks (GRNN)
AB  - Accurate weather forecasting plays a vital role for planning day to day activities. Neural network has been use in numerous meteorological applications including weather forecasting. A neural network model has been developed for weather forecasting, based on various factors obtained from meteorological experts. This study evaluates the performance of Generalized Regression Neural Networks (GRNN) model with Radial Basis Function (RBF) with Back Propagation (BPN) neural network. The back propagation neural network, radial basis function neural network and generalized regression neural networks are used in this study to test the performance in order to investigate which technique for weather forecasting most, effective. The prediction accuracy of GRNN is 96.80%. The results indicate that proposed generalized regression neural networks is better than back propagation neural network and radial basis function.
ER  -