Parametric uncertainty quantification in the Rothermel model with randomised quasi-Monte Carlo methods
Yaning Liu A D E , Edwin Jimenez B , M. Yousuff Hussaini A , Giray Ökten A and Scott Goodrick C
+ Author Affiliations
- Author Affiliations
A Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA.
B Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA.
C USDA Forest Service Center for Forest Disturbance Science, 320 Green Street, Athens, GA 30602, USA.
D Present address: Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA.
E Corresponding author. Email: yaningliu@lbl.gov
International Journal of Wildland Fire 24(3) 307-316 https://doi.org/10.1071/WF13097
Submitted: 11 June 2013 Accepted: 24 September 2014 Published: 7 April 2015
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 keywords: chaparral fuel model, fire propagation, global sensitivity analysis, variance reduction.
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