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Forecasting SO 2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models

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Show simple item record Sánchez, Antonio Bernardo Ordóñez, Celestino Lasheras, Fernando Sánchez de Cos Juez, Francisco Javier Roca Pardiñas, Javier 2019-03-11T08:57:23Z 2019-03-11T08:57:23Z 2013
dc.identifier.citation Abstract and Applied Analysis, 2013, 238-259 (2013) spa
dc.identifier.issn 10853375
dc.identifier.issn 16870409
dc.description.abstract An SO2 emission episode at coal-fired power station occurs when the series of bihourly average of SO2 concentration, taken at 5-minute intervals, is greater than a specific value. Advance prediction of these episodes of pollution is very important for companies generating electricity by burning coal since it allows them to take appropriate preventive measures. In order to forecast SO2 pollution episodes, three different methods were tested: Elman neural networks, autoregressive integrated moving average (ARIMA) models, and a hybrid method combining both. The three methods were applied to a time series of SO2 concentrations registered in a control station in the vicinity of a coal-fired power station. The results obtained showed a better performance of the hybrid method over the Elman networks and the ARIMA models. The best prediction was obtained 115 minutes in advance by the hybrid model. spa
dc.language.iso eng spa
dc.publisher Abstract and Applied Analysis spa
dc.title Forecasting SO 2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models spa
dc.type article spa
dc.rights.accessRights openAccess spa
dc.identifier.doi 10.1155/2013/238259
dc.identifier.editor spa
dc.publisher.departamento Estatística e investigación operativa spa
dc.publisher.grupoinvestigacion Inferencia Estatística, Decisión e Investigación Operativa spa
dc.subject.unesco 3318.01 Minería del Carbón spa
dc.subject.unesco 3308.01 Control de la Contaminación Atmosférica spa
dc.subject.unesco 1209 Estadística spa 2019-03-06T15:08:26Z

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