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ANALYSIS OF ARTIFICIAL NEURAL NETWORK MODELS FOR RAINFALL FORECASTING
TAHER JINWALA [1], AJINKYA GHUMATKAR [2], SURAJ JEURGI [3], TANAJI KHADTARE [4]

Published in: Journal for Advance Research in Applied Sciences
Volume- 4, Issue-1, pp.593-600, Jun 2017
DPI :-> 16.10089.JARAS.2017.V4I1.593600.1795



Abstract
Rainfall forecasting is a very important and challenging task as agricultural activities are totally dependent on rainfall. Also an accurate rainfall forecasting is important for warning floods, pre-planned appropriate use of water resources. Rainfall is a highly complex, non-linear climatic phenomenon. Statistical techniques fail to predict rainfall accurately due to this dynamic nature of climate. Artificial Neural Networks is one of the most prominent methods used for rainfall forecasting. In the present research, prediction of average monthly rainfall over Pune observatory of Maharashtra State in India has been analyzed through artificial neural network models. The paper implements feed-forward neural network, recurrent neural network and cascaded feed-forward neural network by building the networks, training and testing the data sets and finding the number of hidden neurons in layers, training function and learning function for the best performance.

Key-Words / Index Term
rainfall; forecasting; FFNN;RNN;CFFNN

How to cite this article
TAHER JINWALA [1], AJINKYA GHUMATKAR [2], SURAJ JEURGI [3], TANAJI KHADTARE [4] , “ANALYSIS OF ARTIFICIAL NEURAL NETWORK MODELS FOR RAINFALL FORECASTING ”, Journal for Advance Research in Applied Sciences, 4, Issue-1, pp.593-600, Jun 2017. DPI:16.10089.JARAS.V4.I1.1795