RT info:eu-repo/semantics/article
T1 Neural Models for Imputation of Missing Ozone Data in Air-Quality Datasets
A1 Arroyo Puente, Ángel
A1 Herrero Cosío, Álvaro
A1 Tricio Gómez, Verónica
A1 Corchado, Emilio
A1 Woźniak, Michał
K1 Informática
K1 Computer science
AB Ozone is one of the pollutants with most negative efects on human health and in general on the biosphere. Many data-acquisitionnetworks collect data about ozone values in both urban and background areas. Usually, these data are incomplete or corrupt and theimputation of the missing values is a priority in order to obtain complete datasets, solving the uncertainty and vagueness of existingproblems to manage complexity. In the present paper, multiple-regression techniques and Artifcial Neural Network models areapplied to approximate the absent ozone values from fve explanatory variables containing air-quality information. To compare thediferent imputation methods, real-life data from six data-acquisition stations from the region of Castilla y Leon (Spain) are gathered ´in diferent ways and then analyzed. Te results obtained in the estimation of the missing values by applying these techniques andmodels are compared, analyzing the possible causes of the given response.
PB Hindawi
SN 1076-2787
YR 2018
FD 2018
LK http://hdl.handle.net/10259/7261
UL http://hdl.handle.net/10259/7261
LA eng
DS Repositorio Institucional de la Universidad de Burgos
RD 27-abr-2024