Adaptive EEMD-ANN hybrid model for Indian summer monsoon rainfall forecasting

Autor: Kavya Johny, S. Adarsh, Maya L. Pai
Rok vydání: 2020
Předmět:
Zdroj: Theoretical and Applied Climatology. 141:1-17
ISSN: 1434-4483
0177-798X
DOI: 10.1007/s00704-020-03177-5
Popis: 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.
Databáze: OpenAIRE
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