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    Dionysus: a stochastic fire growth scenario generator

    New search for: Han, Lengyi
    New search for: John Braun, W.

    The Prometheus Fire Growth Model is a deterministic wildfire simulator. Given weather, topographical and fuel information, the simulated fire front is plotted at equally spaced times. Unpredictability of fire behavior makes deterministic predictions inaccurate. By statistically modeling the data to which the model equations are fit, it is possible to obtain a distribution of fire front predictions. If the weather forecast is reliable, repeated stochastic simulation is not required, so probability contours require no more computing time than what is required for a single deterministic contour. Copyright © 2013 John Wiley & Sons, Ltd.

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    Dionysus: a stochastic fire growth scenario generator

    New search for: Han, Lengyi
    New search for: John Braun, W.

    The Prometheus Fire Growth Model is a deterministic wildfire simulator. Given weather, topographical and fuel information, the simulated fire front is plotted at equally spaced times. Unpredictability of fire behavior makes deterministic predictions inaccurate. By statistically modeling the data to which the model equations are fit, it is possible to obtain a distribution of fire front predictions. If the weather forecast is reliable, repeated stochastic simulation is not required, so probability contours require no more computing time than what is required for a single deterministic contour. Copyright © 2013 John Wiley & Sons, Ltd.

    Access

    Check access
    Check availability in my library
    Order at Subito €

    Dionysus: a stochastic fire growth scenario generator

    New search for: Han, Lengyi
    New search for: John Braun, W.

    The Prometheus Fire Growth Model is a deterministic wildfire simulator. Given weather, topographical and fuel information, the simulated fire front is plotted at equally spaced times. Unpredictability of fire behavior makes deterministic predictions inaccurate. By statistically modeling the data to which the model equations are fit, it is possible to obtain a distribution of fire front predictions. If the weather forecast is reliable, repeated stochastic simulation is not required, so probability contours require no more computing time than what is required for a single deterministic contour. Copyright © 2013 John Wiley & Sons, Ltd.

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    Check availability in my library
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    Title:

    Dionysus: a stochastic fire growth scenario generator

    Contributors:

    Han, Lengyi (author) / John Braun, W. (author)

    Published in:

    Environmetrics ; 25 ; 431-442

    Publication date:

    2014-09-01

    Size:

    12 pages

    ISSN:

    1099-095X , 1180-4009

    DOI:

    https://doi.org/10.1002/env.2236

    Type of media:

    Article (Journal)

    Type of material:

    Electronic Resource

    Language:

    English

    Keywords:

    burn probability maps , deterministic process model , rate of spread , wildfire growth , Monte Carlo , power transformation , ensemble methods

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