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Artificial Neural Network-Derived Trends in Daily Maximum Surface Ozone Concentrations
Interannual variability in meteorological conditions can confound attempts to identify changes in ozone concentrations driven by reduced precursor emissions. In this paper, a technique is described that attempts to maximize the removal of meteorological variability from a daily maximum ozone time series, thereby revealing longer term changes in ozone concentrations with increased confidence. The technique employs artificial neural network [multilayer perceptron (MLP)] models, and is shown to remove more of the meteorological variability from U.S. ozone data than does a Kolmogorov-Zurbenko (KZ) filter and conventional regression-based technique.
Artificial Neural Network-Derived Trends in Daily Maximum Surface Ozone Concentrations
Interannual variability in meteorological conditions can confound attempts to identify changes in ozone concentrations driven by reduced precursor emissions. In this paper, a technique is described that attempts to maximize the removal of meteorological variability from a daily maximum ozone time series, thereby revealing longer term changes in ozone concentrations with increased confidence. The technique employs artificial neural network [multilayer perceptron (MLP)] models, and is shown to remove more of the meteorological variability from U.S. ozone data than does a Kolmogorov-Zurbenko (KZ) filter and conventional regression-based technique.
Artificial Neural Network-Derived Trends in Daily Maximum Surface Ozone Concentrations
Gardner, Matthew (author) / Dorling, Stephen (author)
Journal of the Air & Waste Management Association ; 51 ; 1202-1210
2001-08-01
9 pages
Article (Journal)
Electronic Resource
Unknown
| Taylor & Francis Verlag | 2004