Predictive uncertainty in environmental modelling

Gavin C. Cawley, Gareth J. Janacek, Malcolm R. Haylock, Stephen R. Dorling

Research output: Chapter in Book/Report/Conference proceedingConference contribution

54 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationNeural Networks
Subtitle of host publicationComputational Intelligence in Earth and Environmental Sciences
Number of pages13
Publication statusPublished - May 2007


  • Predictive uncertainty
  • Environmental modelling
  • Multilayer perceptron
  • Statistics

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