Vázquez-Ramírez, S.; Torres-Ruiz, M.; Quintero, R.; Chui, K.T.; Guzmán Sánchez-Mejorada, C. An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model. Mathematics2023, 11, 3060.
Vázquez-Ramírez, S.; Torres-Ruiz, M.; Quintero, R.; Chui, K.T.; Guzmán Sánchez-Mejorada, C. An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model. Mathematics 2023, 11, 3060.
Vázquez-Ramírez, S.; Torres-Ruiz, M.; Quintero, R.; Chui, K.T.; Guzmán Sánchez-Mejorada, C. An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model. Mathematics2023, 11, 3060.
Vázquez-Ramírez, S.; Torres-Ruiz, M.; Quintero, R.; Chui, K.T.; Guzmán Sánchez-Mejorada, C. An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model. Mathematics 2023, 11, 3060.
Abstract
Several sun models suggest the 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 of 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 of 1.5°C global warming above pre-industrial levels could be helpful to respond to the threat of c.c. and develop sustainably. Many studies have predicted the temperature change 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 the warming. Thus, we propose a finite-time thermodynamic (FTT) approach in the present work. The 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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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