Machine Learning-Driven Extreme Climate Data Processing and Cloud Platform Evolution

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Published: 2026-05-24
Keywords:
Machine Learning, Extreme Climate Data, Cloud Platforms, Data Processing, Scalability

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Haowen Ren
School of Information Engineering, Yancheng Institute of Technology, Yancheng, China
Yuntao Shao
School of Information Engineering, Yancheng Institute of Technology, Yancheng, China
Yongzhe Lu
School of Electronic and Electrical Engineering, Anyang Institute of Technology, Anyang, China

Abstract

This research article explores the integration of machine learning techniques in processing extreme climate data and the evolution of cloud platforms to support such computational demands. The study begins by identifying the challenges associated with handling high-dimensional, heterogeneous climate datasets and the limitations of traditional computational methods. It then introduces a machine learning-driven framework designed to optimize data processing pipelines, improve predictive accuracy, and enhance system scalability. The materials and methods section outlines the experimental setup, including data preprocessing, model training, and cloud-based deployment strategies. Results demonstrate significant improvements in computational efficiency and predictive performance across various climate scenarios. The discussion highlights the implications of these findings for climate science and cloud computing, emphasizing the potential for scalable, real-time analytics. The article concludes by summarizing the contributions and proposing future directions for integrating advanced machine learning models with next-generation cloud platforms.

Article Details

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Vol. 2 No. 1 (2026)

Section

Articles

How to Cite

Machine Learning-Driven Extreme Climate Data Processing and Cloud Platform Evolution. (2026). Hua Xia Xin Zhi, 2(1), 242-253. https://journals.hubblepress.com/index.php/HXXZ/article/view/45

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