Rainfall Prediction Using Artificial Neural Network (ANN) for tarai Region of Uttarakhand
Pooja Yadav
Department of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India.
Atish Sagar *
Division of Agricultural Engineering at Indian Agricultural Research Institute, New Delhi, India.
*Author to whom correspondence should be addressed.
Abstract
Rainfall prediction is clearly of great importance for any country. One would like to make long term prediction, i.e. predict total monsoon rainfall a few weeks or months and in advance short term prediction, i.e. predict rainfall over different locations a few days in advance [1]. Predicted by using its correlation with observed parameter. Several regression and neural network based models are currently available. While Artificial Neural Network provide a great deal of promise, they also embody much uncertainty [2,3]. In this paper, different artificial neural network models have been created for the rainfall prediction of Uttarakhand region in India. These ANN models were created using training algorithms namely, feed-forward back propagation algorithm [4,5]. The number of neurons for all the models was kept at 10. The mean squared error was measured for each model and the best accuracy was obtained by the feed-forward back propagation algorithm with MSE value as low as 0.00547823.
Keywords: Total monsoon rainfall, feed-forward back propagation algorithm, neurons, training algorithms, Artificial Neural Network