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

Area of Science:

• Environmental Science
• Data Science
• Climate Change Research

Background:

• Accurate estimation of carbon dioxide (CO₂) emissions is crucial for climate change mitigation strategies.
• Existing methods for CO₂ emission projection face challenges in precision and capturing complex temporal dependencies.

Purpose of the Study:

• To develop and validate a novel, high-precision machine learning framework for estimating CO₂ emissions.
• To enhance the accuracy of CO₂ emission predictions by integrating advanced data preprocessing and optimization techniques.

Main Methods:

• Utilized Principal Component Analysis (PCA) and Blind Source Separation (BSS) for sophisticated data denoising and feature selection.
• Employed Deep Predictive Recurrent Neural Networks (DPRNNs) to effectively capture short and long-term temporal data dependencies.
• Optimized DPRNN parameters using Nickel-Iron Oxide Anodes (NiOA) to boost prediction accuracy.

Main Results:

• The proposed NiOA-DPRNNs framework achieved a high coefficient of determination (R²) of 0.9736.
• Demonstrated superior performance with the lowest error and fitness values compared to existing models and optimization methods.
• Wilcoxon and ANOVA analyses confirmed the specificity and consistency of the obtained results.

Conclusions:

• The NiOA-DPRNNs framework offers a precise and reliable method for CO₂ emission estimation and projection.
• This approach provides a robust theoretical and empirical foundation for policymakers engaged in combating global warming.
• Future research can extend this framework to include other greenhouse gases and enable real-time tracking for responsive climate action.