Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm
Abstract This paper provides a novel approach to estimating CO₂ emissions with high precision using machine learning based on DPRNNs with NiOA. The data preparation used in the present methodology involves sophisticated stages such as Principal Component Analysis (PCA) as well as Blind Source Separa...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86251-0 |
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| author | Anis Ben Ghorbal Azedine Grine Ibrahim Elbatal Ehab M. Almetwally Marwa M. Eid El-Sayed M. El-Kenawy |
| author_facet | Anis Ben Ghorbal Azedine Grine Ibrahim Elbatal Ehab M. Almetwally Marwa M. Eid El-Sayed M. El-Kenawy |
| author_sort | Anis Ben Ghorbal |
| collection | DOAJ |
| description | Abstract This paper provides a novel approach to estimating CO₂ emissions with high precision using machine learning based on DPRNNs with NiOA. The data preparation used in the present methodology involves sophisticated stages such as Principal Component Analysis (PCA) as well as Blind Source Separation (BSS) to reduce noise as well as to improve feature selection. This purified input dataset is used in the DPRNNs model, where both short and long-term temporal dependencies in the data are captured well. NiOA is utilized to tune those parameters; as a result, the prediction accuracy is quite spectacular. Experimental results also demonstrate that the proposed NiOA-DPRNNs framework gets the highest value of R2 (0.9736), lowest error rates and fitness values than other existing models and optimization methods. From the Wilcoxon and ANOVA analyses, one can approve the specificity and consistency of the findings. Liebert and Ruple firmly rethink this rather simple output as a robust theoretic and empirical framework for evaluating and projecting CO2 emissions; they also view it as a helpful guide for policymakers fighting global warming. Further study can build up this theory to include other greenhouse gases and create methods enabling instantaneous tracking for sophisticated and responsive approaches. |
| format | Article |
| id | doaj-art-216f5a991ee94e99a2e3bc9cfe455f35 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-216f5a991ee94e99a2e3bc9cfe455f352025-02-02T12:20:52ZengNature PortfolioScientific Reports2045-23222025-02-0115112810.1038/s41598-025-86251-0Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithmAnis Ben Ghorbal0Azedine Grine1Ibrahim Elbatal2Ehab M. Almetwally3Marwa M. Eid4El-Sayed M. El-Kenawy5Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)Faculty of Artificial Intelligence, Delta University for Science and TechnologySchool of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain PolytechnicAbstract This paper provides a novel approach to estimating CO₂ emissions with high precision using machine learning based on DPRNNs with NiOA. The data preparation used in the present methodology involves sophisticated stages such as Principal Component Analysis (PCA) as well as Blind Source Separation (BSS) to reduce noise as well as to improve feature selection. This purified input dataset is used in the DPRNNs model, where both short and long-term temporal dependencies in the data are captured well. NiOA is utilized to tune those parameters; as a result, the prediction accuracy is quite spectacular. Experimental results also demonstrate that the proposed NiOA-DPRNNs framework gets the highest value of R2 (0.9736), lowest error rates and fitness values than other existing models and optimization methods. From the Wilcoxon and ANOVA analyses, one can approve the specificity and consistency of the findings. Liebert and Ruple firmly rethink this rather simple output as a robust theoretic and empirical framework for evaluating and projecting CO2 emissions; they also view it as a helpful guide for policymakers fighting global warming. Further study can build up this theory to include other greenhouse gases and create methods enabling instantaneous tracking for sophisticated and responsive approaches.https://doi.org/10.1038/s41598-025-86251-0CO2 emissionsDual-path recurrent neural networksNinja optimizerMachine learningEnvironmental forecastingMetaheuristics |
| spellingShingle | Anis Ben Ghorbal Azedine Grine Ibrahim Elbatal Ehab M. Almetwally Marwa M. Eid El-Sayed M. El-Kenawy Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm Scientific Reports CO2 emissions Dual-path recurrent neural networks Ninja optimizer Machine learning Environmental forecasting Metaheuristics |
| title | Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm |
| title_full | Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm |
| title_fullStr | Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm |
| title_full_unstemmed | Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm |
| title_short | Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm |
| title_sort | predicting carbon dioxide emissions using deep learning and ninja metaheuristic optimization algorithm |
| topic | CO2 emissions Dual-path recurrent neural networks Ninja optimizer Machine learning Environmental forecasting Metaheuristics |
| url | https://doi.org/10.1038/s41598-025-86251-0 |
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