Exploitation of Advanced Deep Learning Methods and Feature Modeling for Air Quality Prediction
Bibliographic record
Abstract
Air pollution is a major issue because Particulate Matter (PM) has a substantially higher effect on human health than other pollutants. Air Quality (AQ) prediction has become critical recently to take action to reduce pollution. This research introduces a unique methodology for assessing the effectiveness of PM10 and PM2.5. Enhanced spatial, temporal sequence-Improved Sparse Auto Encoder with Deep Learning (EISAE-DL) has been proposed to predict AQ affected by the prolonged dependency of air pollution congregation. However, Long Short-Term Memory (LSTM) used in EISAE-DL has suffered from the learning of a long-term dependent sequence of the training dataset. In addition, it is hard to create very reliable AQ forecasts at higher periodic frequencies, such as daily, weekly, or even monthly. This paper proposes Transfer learning (TL) in a Stacked Bidirectional and Unidirectional LSTM to solve the learning issue in LSTM for long-term dependencies. So, EISAE-DL with TL and modified LSTM model is named as EISAE-Deep Transfer Learning (EISAE-DTL). TL with a modified structure can handle large-size datasets effectively. However, training time is increased more than twice for non-transfer learning way of modeling due to TL, Wasserstein Distance-based adversarial learning is proposed in EISAE-DTL to decrease the variances among AQ data collected from any two sites. The proposed work is named EISAE- Enhanced DTL (EISAE-EDTL). The developed EISAE-DTL and EISAE-EDTL models are compared and analyzed with the performance of existing algorithms EISAE-DL, ISAE-DL, TL-BLSTM, MMSL, and ST-DNN. The experimental findings demonstrate the accuracy, precision, sensitivity, specificity, Area Under Curve (AUC), and Matthew's correlation coefficient of the proposed model performs admirably and improves present condition approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".