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Published May 2015 | public
Journal Article

Parametric uncertainty quantification in the Rothermel model with randomised quasi-Monte Carlo methods

Abstract

Rothermel's wildland surface fire model is a popular model used in wildland fire management. The original model has a large number of parameters, making uncertainty quantification challenging. In this paper, we use variance-based global sensitivity analysis to reduce the number of model parameters, and apply randomised quasi-Monte Carlo methods to quantify parametric uncertainties for the reduced model. The Monte Carlo estimator used in these calculations is based on a control variate approach applied to the sensitivity derivative enhanced sampling. The chaparral fuel model, selected from Rothermel's 11 original fuel models, is studied as an example. We obtain numerical results that improve the crude Monte Carlo sampling by factors as high as three orders of magnitude.

Additional Information

© 2015 IAWF. Received 11 June 2013, accepted 24 September 2014, published online 7 April 2015.

Additional details

Created:
August 22, 2023
Modified:
October 23, 2023