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

Author

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  • Sebastián Vázquez-Ramírez

    (Instituto Politécnico Nacional, CIC, UPALM-Zacatenco, Mexico City 07320, Mexico)

  • Miguel Torres-Ruiz

    (Instituto Politécnico Nacional, CIC, UPALM-Zacatenco, Mexico City 07320, Mexico)

  • Rolando Quintero

    (Instituto Politécnico Nacional, CIC, UPALM-Zacatenco, Mexico City 07320, Mexico)

  • Kwok Tai Chui

    (Department of Electronic Engineering and Computer Science, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China)

  • Carlos Guzmán Sánchez-Mejorada

    (Instituto Politécnico Nacional, CIC, UPALM-Zacatenco, Mexico City 07320, Mexico)

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.

Suggested Citation

  • Sebastián Vázquez-Ramírez & Miguel Torres-Ruiz & Rolando Quintero & Kwok Tai Chui & Carlos Guzmán Sánchez-Mejorada, 2023. "An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model," Mathematics, MDPI, vol. 11(14), pages 1-26, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3060-:d:1191430
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    References listed on IDEAS

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    1. Vahid Farhangmehr & Juan Hiedra Cobo & Abdolmajid Mohammadian & Pierre Payeur & Hamidreza Shirkhani & Hanifeh Imanian, 2023. "A Convolutional Neural Network Model for Soil Temperature Prediction under Ordinary and Hot Weather Conditions: Comparison with a Multilayer Perceptron Model," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    2. Caldas, M. & Alonso-Suárez, R., 2019. "Very short-term solar irradiance forecast using all-sky imaging and real-time irradiance measurements," Renewable Energy, Elsevier, vol. 143(C), pages 1643-1658.
    3. Kamadinata, Jane Oktavia & Ken, Tan Lit & Suwa, Tohru, 2019. "Sky image-based solar irradiance prediction methodologies using artificial neural networks," Renewable Energy, Elsevier, vol. 134(C), pages 837-845.
    4. Lin, Fan & Zhang, Yao & Wang, Jianxue, 2023. "Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods," International Journal of Forecasting, Elsevier, vol. 39(1), pages 244-265.
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    Cited by:

    1. Fangyi Xu & Jihong Wang, 2025. "Harnessing Hybridized Machine Learning Algorithms for Sustainable Smart Production: A Case Study of Solar PV Energy in China," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 3214-3264, March.

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