Assessing the Potential of Artificial Intelligence (Artificial Neural Networks) in Predicting the Spatiotemporal Pattern of Wildfire-Generated PM2.5 Concentration
Bibliographic record
Abstract
To evaluate the health effects of wildfire smoke, it is crucial to identify reliable models, at fine spatiotemporal resolution, of exposure to wildfire-generated PM2.5. To this end, satellite-drived aerosol optical depth (AOD) measurements are widely used in exposure models, providing long and short-term PM2.5 predictions. Multiple regression models, specifically land use regression (LUR), incorporating AOD images have shown good potential for estimating long-term PM2.5 exposure, but less so for short-term predictions. In this study, we developed artificial neural networks (ANNs) and, in particular, multilayer perceptron (MLP) by integrating ground-based PM2.5 measurements with AOD images and meteorological and spatial variables. Moreover, we used spatial- and temporal-ANNs to investigate and compare the ANNs’ ability to predict different PM2.5 concentration levels caused by abrupt spatial and temporal changes in fire smoke. The study herein analyzes and compares the viability of previously established neural network approaches in predicting short-term PM2.5 exposure during the 2014–2017 wildfire seasons in the province of Alberta, Canada. The performance of ANNs is also compared to classical models, including simple correlation (PM2.5 vs. AOD) and multiple linear regression (MLR) including meteorological and land-use predictors (MET_AOD_LUR). Our study shows that ANN achieved a 15% to 113% R2 increase compared to competing models.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".