Kriged Kalman Filtering for Predicting the Spatio-Temporal Wildfire Temperature Process Evolution
Bibliographic record
Abstract
Existing wildfire evolution models have been mostly developed in a deterministic modelling framework. As a complementary alternative, in this paper, we study the problem of predicting the spatio-temporal wildfire temperature process evolution using the Kriged Kalman filtering framework. In particular, the spatio-temporal temperature process is decomposed into a mean process and a residual process. The mean temperature process is further decomposed into a linear combination of fixed spatial basis functions with stochastic temporal coefficients that evolve in time, whereas the residual temperature process is modelled as a zero-mean spatio-temporal Gaussian process. In the Kriged Kalman filtering framework, one challenge is to specify a suitable set of spatial basis functions that gives a good representation of the process. By solving the partial differential equation that governs the principal heat transfer mechanisms driving the wildfire evolution, we show that the spatio-temporal mean temperature process associated with a wildfire evolving in a finite spatial domain under certain prescribed conditions can be approximated by a Fourier series. One key novelty in our work is that we explicitly incorporate the physics of the wildfire evolution process in deriving a suitable choice of spatial basis functions that approximate the mean temperature process. We also derive an evolution model for the temporal coefficients of the mean temperature process based on the proposed heat transfer partial differential equation. Finally, we demonstrate the potential of the proposed Kriged Kalman filtering framework in simulations on temperature data generated by a simplified physical wildfire evolution model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".