Monthly rainfall prediction using wavelet regression and neural network: an analysis of 1901-2002 data, Assam, India

Rainfall is a principal element of the hydrological cycle and its variability is important from both the scientific as well as practical point of view. Wavelet regression (WR) technique is proposed and developed to analyze and predict the rainfall forecast in this study. The WR model is improved combining two methods, discrete wavelet transform and linear regression model. This study uses rainfall data from 21 stations in Assam, India over 102 years from 1901 to 2002. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The root mean square errors (RMSE), N-S index, and correlation coefficient (R) statistics were used for evaluating the accuracy of the WR models. The accuracy of the WR models was then compared with those of the artificial neural networks (ANN) models. The results of monthly rainfall series modeling indicate that the performances of wavelet regression models are found to be more accurate than the ANN models.