Maximum likelihood cost functions for neural network models of air quality data

Dorling, Stephen R., Foxall, Robert J., Mandic, Danilo P. and Cawley, Gavin C. ORCID: https://orcid.org/0000-0002-4118-9095 (2003) Maximum likelihood cost functions for neural network models of air quality data. Atmospheric Environment, 37 (24). pp. 3435-3443. ISSN 1352-2310

Full text not available from this repository. (Request a copy)

Abstract

The prediction of episodes of poor air quality using artificial neural networks is investigated, concentrating on selection of the most appropriate cost function used in training. Different cost functions correspond to different distributional assumptions regarding the data, the appropriate choice depends on whether a forecast of absolute pollutant concentration or prediction of exceedence events is of principle importance. The cost functions investigated correspond to logistic regression, homoscedastic Gaussian (i.e. conventional sum-of-squares) regression and heteroscedastic Gaussian regression. Both linear and nonlinear neural network architectures are evaluated. While the results presented relate to a dataset describing the daily time-series of the concentration of surface level ozone (O3) in urban Berlin, the methods applied are quite general and applicable to a wide range of pollutants and locations. The heteroscedastic Gaussian regression model outperforms the other nonlinear methods investigated; however, there is little improvement resulting from the use of nonlinear rather than linear models. Of greater significance is the flexibility afforded by the nonlinear heteroscedastic Gaussian regression model for a range of potential end-users, who may all have different answers to the question: “What is more important, correctly predicting exceedences or avoiding false alarms?”.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA
UEA Research Groups:	Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences
Depositing User:	EPrints Services
Date Deposited:	01 Oct 2010 13:41
Last Modified:	24 Sep 2024 10:08
URI:	https://ueaeprints.uea.ac.uk/id/eprint/3059
DOI:	10.1016/S1352-2310(03)00323-6

Actions (login required)

View Item