A hybrid intelligent algorithm for optimum forecasting of CO<sub>2</sub> emission in complex environments: the cases of Brazil, Canada, France, Japan, India, UK and US
Bibliographic record
Abstract
This study presents a hybrid meta-modeling algorithm for optimum carbon dioxide (CO2) emission estimation. It is composed of artificial neural network (ANN), fuzzy linear regression (FLR), and conventional regression (CR). Different FLR models are considered to cover the latest algorithms and viewpoints. ANN with different training algorithms and transfer functions is also applied to data sets. The proposed hybrid algorithms uses analysis of variance (ANOVA), and mean absolute percentage error (MAPE) to select between ANN, FLR or conventional regression for future CO2 emission estimation. The intelligent algorithm of this study is then applied to estimate CO2 emission in seven countries including India, Canada, Brazil, France, Japan, United Kingdom and United States. Different models are selected as preferred model for annual CO2 emission estimation in these countries. The preferred model for India, Brazil, United Kingdom and United States is selected as FLR whereas the preferred model for CO2 emission estimation in Japan, Canada and France is ANN. This shows how adopting the proposed hybrid algorithm could help in selecting the preferred model between FLR, ANN and CR in order to cover possible noise, complexity and ambiguity. This is the first study that utilizes a hybrid algorithm based on ANN, FLR and CR for accurate and optimum long term CO2 emission estimation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".