Forecasting Carbon Dioxide Emission Using Hybrid Machine Learning and Nonlinear Data Decomposition Methods

The project "Forecasting Carbon Dioxide Emission Using Hybrid Machine Learning and Nonlinear Data Decomposition Methods" aims to develop an advanced, robust solution for accurately predicting CO2 emissions across various sectors. This project utilizes a comprehensive dataset containing key features such as waste disposal, population demographics, agricultural activities, industrial emissions, and land-use changes. The goal is to forecast carbon emissions at a granular level, providing crucial insights that can inform decision-making for policy development, resource allocation, and environmental strategies. The models employed for this study include powerful machine learning algorithms such as XGBoost, LightGBM, Random Forest, and Artificial Neural Networks (ANN), selected for their ability to handle large, complex datasets with high predictive accuracy. Additionally, advanced ensemble methods, including stacking and blending, are integrated to optimize performance and improve the overall prediction reliability. The stacking model combines base models like LightGBM, Decision Trees, and Support Vector Regression, while the blending model incorporates similar base models but uses different integration techniques to further refine predictions. Nonlinear data decomposition methods are also incorporated to enhance feature extraction, capture hidden relationships in the data, and improve model robustness. The primary objective of this project is not only to predict CO2 emissions with high precision but also to provide valuable insights that can help identify key contributing factors, which can be targeted for emission reduction strategies. This research holds significant potential for advancing climate change mitigation efforts through accurate forecasting and data-driven decision-making.

Keywords: CO2 emissions, machine learning, XGBoost, LightGBM, Random Forest, ANN, stacking, blending, nonlinear data decomposition, predictive modelling.