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Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management
Traditional ML methods used for fire modeling offer computational speedup but struggle with physically inconsistent predictions, biased predictions due to class imbalance, biased estimates for fire spread metrics (e. g., burned area, rate of spread), and generalizability in out-of-distribution wind conditions.
Approximate Bayesian Computation for Physical Inverse Modeling
Using these TFT models to draw inference involves estimating parameters used to fit to the experimental data.
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