Climate Change Forecasting using Machine Learning Algorithms

Abstract

Abstract: This is an extremely contentious subject of study as many people across the world don't believe that humans are responsible for climate change. There will be an urgent requirement for adaptation as a result of the effects of climate change on human society, as this is the only way that humans can hope to survive the increasingly chaotic weather of the future. The rapid development of machine learning (ML) algorithms has sparked innovations in numerous fields of study and has even been proposed as improving climate studies. Since global warming has an effect on not just people but also on many other kinds of animals, efforts to bring it down may benefit everyone on Earth. To examine the global climate's development since 1800, we use machine learning methods. From land average temperature to land average temperature uncertainty (95% CI around the mean), land maximum temperature to land average temperature uncertainty, land minimum temperature to land average temperature uncertainty, land and ocean average temperature to land and ocean average temperature uncertainty, and more. Kaggle datasets show a wide range of temperature change metrics since 1750. Another dataset measures the average worldwide concentration of carbon monoxide since 1958; it was compiled by the Earth System Research Laboratory of the United States Government. We propose focusing equally on machine learning and artificial intelligence to better interpret and profit from current data and simulation. Our findings provide insight into the difficulties and potential benefits of data-driven climate modeling, particularly about the future integration of increasingly enormous model datasets.