Document


Title

Parametric uncertainty quantification in the Rothermel model with randomised quasi-Monte Carlo methods
Document Type: Journal Article
Author(s): Yaning Liu; Edwin Jimenez; M. Yousuff Hussaini; Giray Okten; Scott L. Goodrick
Publication Year: 2015

Cataloging Information

Keyword(s):
  • chaparral
  • chaparral fuel model
  • fire management
  • fire propagation
  • fuel models
  • GSA - global sensitivity analysis
  • statistical analysis
  • surface fires
  • variance components
  • variance reduction
Record Maintained By:
Record Last Modified: July 2, 2019
FRAMES Record Number: 53754
Tall Timbers Record Number: 31250
TTRS Location Status: In-file
TTRS Call Number: Journals - I
TTRS Abstract Status: Fair use, Okay, Reproduced by permission

This bibliographic record was either created or modified by the Tall Timbers Research Station and Land Conservancy and is provided without charge to promote research and education in Fire Ecology. The E.V. Komarek Fire Ecology Database is the intellectual property of the Tall Timbers Research Station and Land Conservancy.

Description

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.

Online Link(s):
Citation:
Liu, Yaning; Jimenez, Edwin; Hussaini, M. Yousuff; Okten, Giray; Goodrick, Scott L. 2015. Parametric uncertainty quantification in the Rothermel model with randomised quasi-Monte Carlo methods. International Journal of Wildland Fire 24(3):307-316.