Deep Learning-Based Prediction Model for Greenhouse Gas Emission in Gulf-Coast Communities

The extensive evidence indicates that human activities have played a major role in causing global warming since the early 20th century. While natural factors also influence Earth's radiative energy balance, human-induced greenhouse gas emissions, such as carbon dioxide, methane, and nitrous oxide, have contributed significantly to trapping Earth's outgoing energy and altering the planet's climate and weather patterns. This has led to changes in various climate aspects, posing risks to human health, agriculture, water resources, wildlife, and coastal areas.

In this research, we aimed to develop a deep learning-based predictive model for greenhouse gas concentrations in the Gulf-Coast region using real-time data from NASA's PANDORA and AERONET networks. By focusing on major greenhouse gases like carbon dioxide, nitrous oxide, ozone, water vapor, and volatile organic compounds such as HCHO, we sought to gain a deeper understanding of their environmental impacts. Our design employed statistical methods, including correlation, regression, and time series modeling, to analyze the relationships between these variables and predict future precipitation levels. Notably, our analysis indicated that carbon dioxide is projected to be the fastest-growing anthropogenic greenhouse gas, largely due to increased atmospheric loadings from human activities, such as the combustion of fossil fuels. We utilized regression and time series machine learning models, which demonstrated high accuracy in forecasting precipitation levels. In particular, the time series model surpassed the regression model in performance. Leveraging machine learning-based visualizations provided valuable insights into data trends and occurrences, enabling a deeper understanding of the dynamics at play. Additionally, a comprehensive performance comparison of the regression and time series models, based on metrics like the R₂ score, facilitated an evaluation of their suitability for practical applications.

The findings from our study hold significant implications for policymakers and stakeholders in the Gulf-Coast region and beyond. Emphasizing the importance of monitoring and forecasting greenhouse gas emissions, our research underscores the need to mitigate the impacts of climate change on various aspects of the environment and human society.