Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
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 (Autor:in) / Dorling, Stephen (Autor:in)
Journal of the Air & Waste Management Association ; 51 ; 1202-1210
01.08.2001
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
| Taylor & Francis Verlag | 2004