Assessing the Impact Of Time Intervals On PM2.5 Prediction Using LSTM Neural Networks
Bibliographic record
Abstract
Air pollution is a global issue with profound implications to human health and environmental sustainability. Especially, PM2.5, which refers to particulate matter with a size of 2.5 microns or smaller, poses significant health risks. Therefore, accurately predicting PM2.5 levels is crucial. In this aim, various machine-learning methods are employed for PM2.5 prediction. The problem arises from the use of collected datasets at varied time intervals (5 minutes, 15 minutes, 1 hour, etc.), resulting in inconsistency across predictive approaches. While shorter time intervals seems to offer heightened sensitivity to changes, implying simpler prediction, this presumption may not be universally applicable. This paper investigates the impact of time intervals on PM2.5 prediction through a comparative analysis of datasets collected at different intervals (15 minutes, 1 hour, and 8 hours) using Long Short Term Memory Neural Networks (LSTM). By leveraging statistical analysis and predictive modeling techniques, we assess the dataset's distributions and evaluate prediction model performance. This study underscores the importance of interval selection in PM2.5 prediction and informs future efforts to enhance air quality surveillance and public health protection measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".