Integration of Dynamic Slow Feature Analysis and Deep Neural Networks for Subway Indoor PM₂.₅ Prediction
Bibliographic record
Abstract
This study addresses the limitations in data-driven PM2.5 concentration prediction, which typically depends on statistical relationships with other factors, posting challenges in processing. To address the high costs associated with hardware-based monitoring, we introduced a novel hybrid model that synergizes dynamic slow feature analysis (DSFA), long short-term memory (LSTM) network, and convolutional block attention module (CBAM). The DSFA effectively resolves time lag issues prevalent in real industrial processes. When provided as input to the LSTM for training, the retrieved slow features effectively extract dynamic information from the data while minimizing complexity. Subsequently, CBAM adaptively adjusts feature weights, leading to refined prediction results. Comparative analysis reveals that our DSFA-LSTM-CBAM model outperforms conventional deep learning models, including partial least square (PLS), CNN, standard LSTM, and other hybrid models in predictive accuracy. Specifically, the model achieves a 45.6% reduction in root mean square error (RMSE) compared to the single LSTM model, and a 12.4% improvement in the coefficient of determination relative to the hybrid PCA-LSTM. In addition, this hybrid model demonstrates an enhanced capacity for handling nonlinearity and time-variability in time series data and exhibits strong robustness, marking a significant advancement in indoor air quality (IAQ) modeling.
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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) | 0.000 | 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".