Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning
This paper investigates the application of three nature-inspired optimisation algorithms – SHO, MFO, and GOA – combined with four machine learning methods – Gaussian Processes, Linear Regression, MLP, and Random Forest – to enhance carbon dioxide emission prediction in the OECD – Asia and Oceania re...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2024.2391988 |
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| author | Loke Kok Foong Vojtech Blazek Lukas Prokop Stanislav Misak Farruh Atamurotov Nima Khalilpoor |
| author_facet | Loke Kok Foong Vojtech Blazek Lukas Prokop Stanislav Misak Farruh Atamurotov Nima Khalilpoor |
| author_sort | Loke Kok Foong |
| collection | DOAJ |
| description | This paper investigates the application of three nature-inspired optimisation algorithms – SHO, MFO, and GOA – combined with four machine learning methods – Gaussian Processes, Linear Regression, MLP, and Random Forest – to enhance carbon dioxide emission prediction in the OECD – Asia and Oceania region. The study uses historical carbon dioxide emissions data, socioeconomic indicators such as GDP, population density, energy consumption, and urbanisation rates, and environmental indicators such as temperature, precipitation, and forest cover. Through comprehensive experimentation, the study evaluates the performance of each combination, revealing varying effectiveness levels. The MFO-MLP combination achieved the highest accuracy with R2 values of 0.9996 and 0.9995 and RMSE values of 11.7065 and 12.8890 for the training and testing datasets, respectively. The GOA-MLP configuration achieved R2 values of 0.9994 and 0.99934 and RMSE values of 15.01306 and 14.59333. The SHO-MLP combination, while effective, showed lower performance with R2 values of 0.9915 and 0.9946 and RMSE values of 55.4516 and 41.575. The findings suggest hybrid techniques can significantly enhance prediction accuracy compared to conventional methods. This research provides valuable insights for policymakers and stakeholders, indicating that optimised machine learning models can support more informed and effective environmental policy-making and sustainability efforts in the OECD – Asia and Oceania region. Future research should explore additional optimisation algorithms and ensemble techniques to improve prediction robustness and accuracy. These findings offer a robust tool for policymakers to forecast emissions more accurately, aiding in developing targeted strategies to reduce carbon footprints and achieve climate goals. |
| format | Article |
| id | doaj-art-a3277f92ba574c5ca1937d40543d1c74 |
| institution | Kabale University |
| issn | 1994-2060 1997-003X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj-art-a3277f92ba574c5ca1937d40543d1c742024-12-09T09:43:46ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2024-12-0118110.1080/19942060.2024.2391988Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learningLoke Kok Foong0Vojtech Blazek1Lukas Prokop2Stanislav Misak3Farruh Atamurotov4Nima Khalilpoor5Institute of Research and Development, Duy Tan University, Da Nang, VietnamENET Centre, VSB—Technical University of Ostrava, Ostrava, Czech RepublicENET Centre, VSB—Technical University of Ostrava, Ostrava, Czech RepublicENET Centre, VSB—Technical University of Ostrava, Ostrava, Czech RepublicFaculty of Pedagogy, Shahrisabz State Pedagogical Institute, Shahrisabz, UzbekistanDepartment of Energy Engineering, Graduate School of the Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, IranThis paper investigates the application of three nature-inspired optimisation algorithms – SHO, MFO, and GOA – combined with four machine learning methods – Gaussian Processes, Linear Regression, MLP, and Random Forest – to enhance carbon dioxide emission prediction in the OECD – Asia and Oceania region. The study uses historical carbon dioxide emissions data, socioeconomic indicators such as GDP, population density, energy consumption, and urbanisation rates, and environmental indicators such as temperature, precipitation, and forest cover. Through comprehensive experimentation, the study evaluates the performance of each combination, revealing varying effectiveness levels. The MFO-MLP combination achieved the highest accuracy with R2 values of 0.9996 and 0.9995 and RMSE values of 11.7065 and 12.8890 for the training and testing datasets, respectively. The GOA-MLP configuration achieved R2 values of 0.9994 and 0.99934 and RMSE values of 15.01306 and 14.59333. The SHO-MLP combination, while effective, showed lower performance with R2 values of 0.9915 and 0.9946 and RMSE values of 55.4516 and 41.575. The findings suggest hybrid techniques can significantly enhance prediction accuracy compared to conventional methods. This research provides valuable insights for policymakers and stakeholders, indicating that optimised machine learning models can support more informed and effective environmental policy-making and sustainability efforts in the OECD – Asia and Oceania region. Future research should explore additional optimisation algorithms and ensemble techniques to improve prediction robustness and accuracy. These findings offer a robust tool for policymakers to forecast emissions more accurately, aiding in developing targeted strategies to reduce carbon footprints and achieve climate goals.https://www.tandfonline.com/doi/10.1080/19942060.2024.2391988CO2 emissionenvironmental policymetaheuristic algorithmOECD |
| spellingShingle | Loke Kok Foong Vojtech Blazek Lukas Prokop Stanislav Misak Farruh Atamurotov Nima Khalilpoor Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning Engineering Applications of Computational Fluid Mechanics CO2 emission environmental policy metaheuristic algorithm OECD |
| title | Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning |
| title_full | Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning |
| title_fullStr | Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning |
| title_full_unstemmed | Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning |
| title_short | Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning |
| title_sort | improve carbon dioxide emission prediction in the asia and oceania oecd nature inspired optimisation algorithms versus conventional machine learning |
| topic | CO2 emission environmental policy metaheuristic algorithm OECD |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2024.2391988 |
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