Journal Article

Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm

Elbatal, Ibrahim, Eid, Marwa M., Almetwally, Ehab M., Grine, Azedine, Ghorbal, Anis Ben, El-Kenawy, El-Sayed M.

2025

Overview

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 R 2 (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 CO 2 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.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/114/1305 / 631/114/1314 / 631/114/2164 / Artificial intelligence / Carbon dioxide / Carbon dioxide emissions / Climate change / CO2 emissions / Datasets / Deep learning / Dual-path recurrent neural networks / Emission standards / Emissions / Environmental forecasting / Feature selection / Global warming / Greenhouse gases / Human influences / Humanities and Social Sciences / Machine learning / Metaheuristics / multidisciplinary / Neural networks / Ninja optimizer / Noise reduction / Optimization algorithms / Optimization techniques / Principal components analysis / Science / Science (multidisciplinary) / Variance analysis