Application of Feed Forward Backpropagation Neural Network in Monthly Rainfall Prediction

Abstract

Rainfall is one of the most critical parameters in a hydrological model. A few models have been created to investigate and predict the rainfall conjecture. in recent years, soft computing models like Artificial Neural Network (ANN) have been widely used to model a complex hydrological processes. In this paper an attempt has been made to find an alternative feed forward backpropagation neural network (FFBNN) architecture for rainfall prediction. The FFBNN with 12-10-5-1 architecture have been attempted and demonstrated with a case study on Tukad Mati watershed. The developed model on rainfall prediction pattern have been calibrated and validated with appropriate statistical methods. For model performance criteria used mean square error (MSE) and coefisient correlation (R) as an indicator. It was found that the FFBNN are capable in modelling monthly rainfall prediction in the Tukad Mati watershed with 0,011913 for average of MSE and 0,85769 for average of R testing value from three rain gauge station. The FFBNN approach could provide a very effective to solve problems in monthly rainfall prediction.