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 characterised by a noise process that may be heteroscedastic (having input dependent variance) and/or non-Gaussian. The aim of this paper is to review an existing methodology for estimating predictive uncertainty in such situations, and more importantly 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 and some areas suggested where further research may provide significant benefits.
['Gavin C. Cawley', 'Malcolm Haylock', 'Stephen R. Dorling']
Predictive Uncertainty in Environmental Modelling