Cawley, GC, Janacek, GJ, Haylock, MR and Dorling, SR (2007) UNSPECIFIED In: UNSPECIFIED 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
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Science > School of Environmental Sciences
Related URLs:	http://www.sciencedirect.com/science/art...
Depositing User:	Rosie Cullington
Date Deposited:	26 Feb 2011 18:55
Last Modified:	13 Mar 2019 10:55
URI:	https://ueaeprints.uea.ac.uk/id/eprint/25130
DOI:	10.1016/j.neunet.2007.04.024

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