RT Journal Article
T1 Functional Location-Scale Model to Forecast Bivariate Pollution Episodes
A1 Oviedo de la Fuente, Manuel
A1 Ordóñez, Celestino
A1 Roca Pardiñas, Javier
K1 Pollution episodes
K1 Functional data
K1 Bivariate analysis
K1 Uncertainty region
K1 Generalized additive models
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 MDPI
YR 2020
FD 2020
LK http://hdl.handle.net/10347/23639
UL http://hdl.handle.net/10347/23639
LA eng
NO Oviedo-de La Fuente, M.; Ordóñez, C.; Roca-Pardiñas, J. Functional Location-Scale Model to Forecast Bivariate Pollution Episodes. Mathematics 2020, 8, 941
NO The authors acknowledge financial support from: (1) UO-Proyecto Uni-Ovi (PAPI-18-GR-2014-0014), (2) Project MTM2016-76969-P from Ministerio de Economía y Competitividad—Agencia Estatal de Investigación and European Regional Development Fund (ERDF) and IAP network StUDyS from Belgian Science Policy, (3) Nuevos avances metodológicos y computacionales en estadística no-paramétrica y semiparamétrica—Ministerio de Ciencia e Investigación (MTM2017-89422-P)
DS Minerva
RD 22 abr 2026