RT Journal Article
T1 Functional location-scale model to forecast bivariate pollution episodes
A1 Oviedo de la Fuente, Manuel
A1 Ordóñez Galán, Celestino
A1 Roca Pardiñas, Javier
K1 1209.03 Análisis de Datos
K1 2509.02 Contaminación Atmosférica
K1 3308.01 Control de la Contaminación Atmosférica
AB Predicting anomalous emission of pollutants into the atmosphere well in advance is crucial for industries emitting such elements, since it allows them to take corrective measures aimed to avoid such emissions and their consequences. In this work, we propose a functional location-scale model to predict in advance pollution episodes where two pollutants are involved. Functional generalized additive models (FGAMs) are used to estimate the means and variances of the model, as well as the correlation between both pollutants. The method not only forecasts the concentrations of both pollutants, it also estimates an uncertainty region where the concentrations of both pollutants should be located, given a specific level of uncertainty. The performance of the model was evaluated using real data of SO     2     and NO     x     emissions from a coal-fired power station, obtaining good results.
PB Mathematics
SN 22277390
YR 2020
FD 2020-06-08
LK http://hdl.handle.net/11093/1845
UL http://hdl.handle.net/11093/1845
LA eng
NO Mathematics, 8(6): 941 (2020)
NO UO-Proyecto Uni-Ovi | Ref. PAPI-18-GR-2014-0014
DS Investigo
RD 04-dic-2024