An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model

Abstract

Several Sun models suggest a radioactive balance, where the concentration of greenhouse gases and the albedo effect are related to the Earth’s surface temperature. There is a considerable increment in greenhouse gases due to anthropogenic activities. Climate change correlates with this alteration in the atmosphere and an increase in surface temperature. Efficient forecasting of climate change and its impacts could be helpful to respond to the threat of c.c. and develop sustainably. Many studies have predicted temperature changes in the coming years. The global community has to create a model that can realize good predictions to ensure the best way to deal with this warming. Thus, we propose a finite-time thermodynamic (FTT) approach in the current work. FTT can solve problems such as the faint young Sun paradox. In addition, we use different machine learning models to evaluate our method and compare the experimental prediction and results.

Keywords

• clustering
• machine learning
• greenhouse gas
• finite-time thermodynamics
• climate change