Optimized Spatial Ensemble Approaches for Approximating the Fire Weather Index with Interpolation
Bibliographic record
Abstract
Accurate interpolation of meteorological data is essential for wildland fire science and management, as it directly impacts the modeling and prediction of fire risk indicators, such as the Fire Weather Index (FWI). In this study, we introduce a spatial ensemble approach that optimally combines multiple interpolation methods, including Inverse Distance Weighting (IDW), Kriging, and Thin Plate Splines (TPS). This ensemble assigns spatially optimized mixing weights that vary across locations to enhance predictive accuracy across our study region of Ontario, Canada. To further improve the ensemble, we developed the Adjusted-Adaptive IDW (AAIDW) method. Adjusted IDW considers shielding effects caused by having multiple observations in the same general direction, while Adaptive IDW adjusts the power parameter based on local data density. AAIDW integrates these two approaches, enhancing spatial predictions by accounting for directional shielding effects while also employing an adaptive power parameter. Our results reveal that both AAIDW and Adjusted IDW outperform Adaptive IDW, highlighting that the shielding effect has a greater influence on improving interpolation accuracy than the adaptive power parameter for FWI predictions in Ontario. Furthermore, the spatial ensemble method consistently outperforms individual interpolation methods, providing more reliable and accurate fire weather predictions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".