Predictive uncertainty in environmental modelling
Cawley, GC, Janacek, GJ, Haylock, MR and Dorling, SR (2007) Predictive uncertainty in environmental modelling. In: Neural Networks. Elsevier, pp. 537-549.
Full text not available from this repository.Abstract
Artificial neural networks have proved an attractive approach to non-linear regression problems arising in environmental modelling, such as statistical downscaling, short-term forecasting of atmospheric pollutant concentrations and rainfall run-off modelling. However, environmental datasets are frequently very noisy and characterized by a noise process that may be heteroscedastic (having input dependent variance) and/or non-Gaussian. The aim of this paper is to review existing methodologies for estimating predictive uncertainty in such situations and, more importantly, to illustrate how a model of the predictive distribution may be exploited in assessing the possible impacts of climate change and to improve current decision making processes. The results of the WCCI-2006 predictive uncertainty in environmental modelling challenge are also reviewed, suggesting a number of areas where further research may provide significant benefits.
Item Type: | Book Section |
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Uncontrolled Keywords: | predictive uncertainty,environmental modelling,multilayer perceptron,statistics |
Faculty \ School: | Faculty of Science > School of Computing Sciences Faculty of Science > School of Environmental Sciences University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology University of East Anglia > Faculty of Science > Research Centres > Water Security Research Centre |
Related URLs: | |
Depositing User: | Rosie Cullington |
Date Deposited: | 26 Feb 2011 18:55 |
Last Modified: | 10 Oct 2017 01:42 |
URI: | https://ueaeprints.uea.ac.uk/id/eprint/25130 |
DOI: | 10.1016/j.neunet.2007.04.024 |
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