%0 Journal Article 
%A Palomares Salas, José Carlos
%A González de la Rosa, Juan José
%A Agüera Pérez, Agustín
%A Sierra Fernández, José María
%A Florencias Oliveros, Olivia
%T Forecasting PM10 in the Bay of Algeciras Based on Regression Models
%D 2019
%@ 2071-1050
%U http://hdl.handle.net/10498/21174
%X Different forecasting methodologies, classified into parametric and nonparametric, were
studied in order to predict the average concentration of PM10 over the course of 24 h. The comparison
of the forecasting models was based on four quality indexes (Pearson’s correlation coefficient,
the index of agreement, the mean absolute error, and the root mean squared error). The proposed
experimental procedure was put into practice in three urban centers belonging to the Bay of Algeciras
(Andalusia, Spain). The prediction results obtained with the proposed models exceed those obtained
with the reference models through the introduction of low-quality measurements as exogenous
information. This proves that it is possible to improve performance by using additional information
from the existing nonlinear relationships between the concentration of the pollutants and the
meteorological variables.
%K time-series forecasting
%K regression models
%K artificial neural networks
%K on-site measurements
%K exogenous information
%~ Universidad de Cádiz