Please use this identifier to cite or link to this item: 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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