RT Journal Article
T1 Forecasting SO 2 pollution incidents by means of Elman artificial neural networks and ARIMA models
A1 Sánchez, Antonio Bernardo
A1 Ordóñez Galán, Celestino
A1 Lasheras, Fernando Sánchez
A1 de Cos Juez, Francisco Javier
A1 Roca Pardiñas, Javier
K1 3318.01 Minería del Carbón
K1 3308.01 Control de la Contaminación Atmosférica
K1 1209 Estadística
AB 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.
PB Abstract and Applied Analysis
SN 10853375
YR 2013
FD 2013
LK http://hdl.handle.net/11093/1213
UL http://hdl.handle.net/11093/1213
LA eng
NO Abstract and Applied Analysis, 2013, 238-259 (2013)
DS Investigo
RD 03-dic-2024