Ozone Pollution Prediction around Industrial Areas Using Fuzzy Neural Network Approach
Bibliographic record
Abstract
This paper presents the prediction of ozone pollution as a function of meteorological parameters including wind speed and direction, relative humidity, temperature, solar intensity, concentration of primary pollutants consisting of methane, carbon monoxide, carbon dioxide, nitrogen oxide, nitrogen dioxide, sulfur dioxide, non‐methane hydrocarbons, and dust around the Shuaiba industrial area in Kuwait by a fuzzy neural network (FNN) modeling approach. A subtractive clustering analysis was performed for the input data to produce a concise representation of the system's behavior leading to the minimum number of rules. In addition, Sugeno–Takagi–Gang fuzzy inference and hybrid algorithm were used to prepare the FNN system. It is perceived that the FNN model is more accurate and reliable than artificial neural network model to forecast the pre‐mentioned concentration. Finally, sensitivity analysis was applied. It was found that temperature, solar radiation, and relative humidity are the dominant parameters affecting the ozone level.
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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 | 0.001 |
| 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".