Prediction of CO maximum ground level concentrations in the Bay of Algeciras, Spain using artificial neural networks.
The region of the Bay of Algeciras is a very industrialized area where very few air pollution studies have been carried out. The main objective of this work has been the use of artificial neural networks (ANNs) as a predictive tool of high levels of ambient carbon monoxide (CO). Two approaches have been used: multilayer perceptron models (MLPs) with backpropagation learning rule and k-Nearest Neighbours (k-nn) classifiers, in order to predict future peaks of carbon monoxide. A resampling strategy with twofold cross-validation allowed the statistical comparison of the different topologies and models considered in the study. The procedure of random resampling permits an adequate and robust multiple comparisons of the tested models and allow us to select a group of best models.
Martín
M L
ML
EPS de Algeciras, Universidad de Cádiz, Avda Ramón Puyol s/n, 11202 Algeciras (Cádiz), Spain. luz.martin@uca.es
Turias
I J
IJ
González
F J
FJ
Galindo
P L
PL
Trujillo
F J
FJ
Puntonet
C G
CG
Gorriz
J M
JM
D000397:Air Pollution / Q000032:analysis*; D002248:Carbon Monoxide / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods; D007221:Industry; D016571:Neural Networks, Computer; D013030:Spain
D002248:Carbon Monoxide