The Application of ANFIS to Prediction of the Amount of Emissions from an Emitter Activity to Reduce Climate Change
Bibliographic record
Abstract
Understanding the links between C02 emissions from fuel consumption and climate change may assist countries in determining the amount of C02 or C02 equivalent emissions, and act accordingly by reformulating new energy policies to reduce emissions and achieve sustainable development. This understanding can help guide countries in developing better energy policies that are designed to reduce C02 and equivalent emissions, while still promoting economic growth and development. To reach the global climate goals set by the United Nations [25], countries must take decisive action to reduce emissions from fuel consumption and energy production while promoting clean energy, which can help mitigate the effects of climate change. Understanding the magnitude of the emissions problem is necessary before coming up with innovative, long-lasting solutions to better comprehend the issue of emissions. An important first step in achieving this understanding is to accurately assess the current levels of emissions from fuel consumption and energy production in a certain country and analyze the amount of these emissions and finally implement strategies to reduce them. Using machine learning technologies such as predictive analytics, and artificial intelligence can help create meaningful insights into the data to understand patterns in emissions that could enable better strategies for reducing emissions. Using a dataset that belongs to the Canadian government, with input data including engine size, number of cylinders, and fuel consumption, an ANFIS model was created to predict emissions. The amount of emissions represents the output. A variety of membership functions were tested in order to gain the best results. Another dataset was generated by applying the Extra-Trees regressor algorithm to the mentioned dataset and retraining the ANFIS model using the generated dataset to achieve better results. The resulting model can then serve as a trustworthy tool to support the application of efficient mitigation strategies and to inform and help policymakers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".