Abstract:
Forecasting of Indian summer monsoon rainfall (ISMR) is a complex problem for the hydrologists and meteorologists. The time
series and data-driven methods have been used as complementary tools for forecasting ISMR against the complex physically
based dynamical models due to scarcity of data and the simplicity of former approaches. The use of hybrid decomposition data–
driven models is the recent improvement among the different approaches for rainfall forecasting, but these approaches differ
significantly in the framework adopted. This paper presents an adaptive hybrid modelling framework so called Adaptive
Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN) model for forecasting ISMR, which
performs the forecasts adaptively as and when new information is added. The performance of the popular EEMD-ANN hybrid
hindcast and forecast experiments in the prediction of All-India SMR and southwest monsoon rainfall of the state of Kerala is
compared with the proposed method. The AEEMD-ANN method achieved a predictive skill of 0.78 and 0.91, respectively for
rainfall predictions for Kerala and All-India. AEEMD-ANN method performed reasonably well in capturing the hydrologic
extremes when compared with EEMD-ANN forecast method, with better accuracy in capturing the drought years. The proposed
method is found to be successful in capturing the extreme low SWM rainfall of year 2002 for All-India and the extreme high
rainfall of Kerala 2018 with an error percentage of 1.09% and 0.52%, respectively.
