Using a statistical model for the description of uncertainties associated with dispersal models

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

    1 Citation (Scopus)

    Abstract

    A quick and accurate prediction of the dispersion of the contaminated material is crucial in case of environmental disasters (Nuclear or chemical accidents). Conventional atmospheric dispersion models (physical models) are widely used for forecasting toxic contamination and obtaining results in real-time with varying degrees of accuracy. These models are deterministic, and one of the most significant problems associated with their use in prediction is the large degree of uncertainty inherent in their predictions. The objective of this work is to present a Bayesian model Smith and French [1993] which embeds a dispersal model in a description of the uncertainties associated with the dispersal model. This both allows the assimilation of data to update current forecasts and also expresses an appropriate degree of uncertainty associated with any forecasts or estimates.

    Original languageEnglish
    Title of host publicationModelling for Environment's Sake
    Subtitle of host publicationProceedings of the 5th Biennial Conference of the International Environmental Modelling and Software Society, iEMSs 2010
    Pages444-451
    Number of pages8
    Publication statusPublished - 2010
    Event5th Biennial Conference of the International Environmental Modelling and Software Society: Modelling for Environment's Sake, iEMSs 2010 - Ottawa, ON, Canada
    Duration: Jul 5 2010Jul 8 2010

    Publication series

    NameModelling for Environment's Sake: Proceedings of the 5th Biennial Conference of the International Environmental Modelling and Software Society, iEMSs 2010
    Volume1

    Other

    Other5th Biennial Conference of the International Environmental Modelling and Software Society: Modelling for Environment's Sake, iEMSs 2010
    Country/TerritoryCanada
    CityOttawa, ON
    Period7/5/107/8/10

    Keywords

    • Atmospheric models
    • Bayesian forecasting
    • Puff models

    ASJC Scopus subject areas

    • Software
    • Environmental Engineering
    • Modelling and Simulation

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