Surrogate-based global sensitivity analysis for turbulence and fire-spotting effects in regional-scale wildland fire modeling

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Date
2018-08Author
Trucchia A.
Egorova V.
Pagnini G.
Rochoux M.C.
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In presence of strong winds, wildfires feature nonlinear behavior, possibly inducing fire-spotting. We present a global sensitivity analysis of a new sub-model for turbulence and fire-spotting included in a wildfire spread model based
on a stochastic representation of the fireline. To limit the number of model evaluations, fast surrogate models based on generalized Polynomial Chaos (gPC) and Gaussian Process are used to identify the key parameters affecting topology and
size of burnt area. This study investigates the application of these surrogates to
compute Sobol’ sensitivity indices in an idealized test case. The wind is known
to drive the fire propagation. The results show that it is a more general leading
factor that governs the generation of secondary fires. This study also compares the
performance of the surrogates for varying size and type of training sets as well as
for varying parameterization and choice of algorithms. The best performance was
achieved using a gPC strategy based on a sparse least-angle regression (LAR) and
a low-discrepancy Halton’s sequence. Still, the LAR-based gPC surrogate tends to
filter out the information coming from parameters with large length-scale, which
is not the case of the cleaning-based gPC surrogate. For both algorithms, sparsity
ensures a surrogate can be built using an affordable number of forward model
evaluations, while the model response is highly multi-scale and nonlinear. Using
a sparse surrogate is thus a promising strategy to analyze new models and its
dependency on input parameters in wildfire applications.