Predictive uncertainty in environmental modelling

Cawley, Gavin C. ORCID: https://orcid.org/0000-0002-4118-9095, Janacek, Gareth J., Haylock, Malcolm R. and Dorling, Stephen R. (2007) Predictive uncertainty in environmental modelling. In: Neural Networks. Elsevier, pp. 537-549.

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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
Uncontrolled Keywords: predictive uncertainty,environmental modelling,multilayer perceptron,statistics,sdg 13 - climate action ,/dk/atira/pure/sustainabledevelopmentgoals/climate_action
Faculty \ School: Faculty of Science > School of Computing Sciences
Faculty of Science > School of Environmental Sciences
Depositing User: Rosie Cullington
Date Deposited: 26 Feb 2011 18:55
Last Modified: 06 Feb 2023 14:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/25130
DOI: 10.1016/j.neunet.2007.04.024

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