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http://hdl.handle.net/10553/48816
Title: | Rainfall forecasting based on ensemble empirical mode decomposition and neural networks | Authors: | Beltrán-Castro, Juan Valencia-Aguirre, Juliana Orozco-Alzate, Mauricio Castellanos-Domínguez, Germán Travieso-González, Carlos M. |
UNESCO Clasification: | 3307 Tecnología electrónica | Keywords: | Forecasting Neural Networks Ensemble Empirical Mode Decomposition Rainfall |
Issue Date: | 2013 | Journal: | Lecture Notes in Computer Science | 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. | URI: | http://hdl.handle.net/10553/48816 | ISBN: | 9783642386787 | ISSN: | 0302-9743 | DOI: | 10.1007/978-3-642-38679-4_47 | Source: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)[ISSN 0302-9743],v. 7902 LNCS, p. 471-480 |
Appears in Collections: | Actas de congresos |
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