Abstract
In this paper a methodology for rainfall forecasting is presented, using the principle of decomposition and ensemble. In the proposed framework, the employed decomposition technique is the Ensemble Empirical Mode Decomposition (EEMD), which divides the original data into a set of simple components. Each component is modeled with a Feed Forward Neural Network (FNN) as a forecasting tool. Finally, the individual forecasting results for all components are combined to obtain the prediction result of the input signal. Experiments were performed on a real-observed rainfall data, and the attained results were compared against a single FNN model for the raw data, showing an improvement on the system performance.
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Beltrán-Castro, J., Valencia-Aguirre, J., Orozco-Alzate, M., Castellanos-Domínguez, G., Travieso-González, C.M. (2013). Rainfall Forecasting Based on Ensemble Empirical Mode Decomposition and Neural Networks. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_47
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DOI: https://doi.org/10.1007/978-3-642-38679-4_47
Publisher Name: Springer, Berlin, Heidelberg
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