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GCGW 2024 ProceedingsBook 554 556 by shantanu kadam is a document available to read on EtoBox.

This conference paper discusses the potential of machine learning techniques in estimating greenhouse gas emissions, highlighting algorithms such as support vector machines, decision trees, and random forests that can achieve prediction accuracies between 75% and 95%. The study emphasizes the advantages of machine learning over traditional methods in analyzing complex datasets and improving emission forecasts. It also notes the growing research activity in countries like the United States, China, and Canada

Author

shantanu kadam

Language

EN