Machine learning based ensemble of satellite, process based model and static calculators to estimate greenhouse gas emissions

Greenhouse gas (GHG) emissions play a significant role in climate change and its adverse impacts on the environment. Accurate estimation of these emissions is crucial for developing effective mitigation strategies. In recent years, advancements in machine learning techniques have opened up new opportunities to improve GHG estimation methodologies by leveraging diverse data sources and modeling approaches. This research presents an innovative ensemble approach that combines satellite data, process-based models, and static calculators to estimate GHG emissions more accurately. The proposed framework harnesses the strengths of each component, resulting in a comprehensive and robust estimation system. The ensemble approach employs machine learning algorithms to integrate and harmonize the outputs from the satellite data, process-based models, and static calculators. The proposed methodology significantly reduces the estimation error to 11.25% compared to the 36.3%, 53.5%, and 17.2% estimation error when satellite, static calculator and process based models were used for estimating the GHG emissions.