Machine Intelligence Framework for Predictive Modeling of CO2 Concentration: A Path to Sustainable Environmental Management

Abstract

Climate change poses critical challenges, necessitating accurate and timely monitoring of CO2 concentrations for sustainable environmental management. Traditional methods for CO2 prediction exhibit limitations in precision and scalability. This paper introduces a novel Machine Intelligence Framework (MIF) specifically designed for predictive modeling of CO2 concentration levels. Leveraging advanced machine learning algorithms and data processing techniques, MIF aims to address the existing research gap by offering enhanced accuracy and adaptability in CO2 forecasting. Motivated by the urgency to combat climate change, this research develops a comprehensive framework integrating predictive modeling with machine intelligence. The methodology involves algorithm design, data integration, and model validation to demonstrate the efficacy of MIF. Results showcase superior performance in CO2 prediction compared to conventional approaches, emphasizing the framework's potential for guiding environmental policies and conservation strategies.