Gardner, Matthew W. and Dorling, Stephen R. (2001) Artificial neural network-derived trends in daily maximum surface ozone concentrations. Journal of the Air and Waste Management Association, 51 (8). pp. 1202-1210. ISSN 2162-2906
Full text not available from this repository. (Request a copy)Abstract
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.
Item Type: | Article |
---|---|
Faculty \ School: | Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA |
UEA Research Groups: | Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences Faculty of Social Sciences > Research Centres > Water Security Research Centre |
Depositing User: | Rosie Cullington |
Date Deposited: | 19 May 2011 09:33 |
Last Modified: | 20 Mar 2023 12:34 |
URI: | https://ueaeprints.uea.ac.uk/id/eprint/30971 |
DOI: | 10.1080/10473289.2001.10464338 |
Actions (login required)
![]() |
View Item |