Large-Scale Natural Disaster Prediction Models and Service Optimization Supported by Cloud Computing

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Published: 2026-05-24
Keywords:
Natural Disaster Prediction, Cloud Computing, Service Optimization, Real-Time Forecasting, Disaster Management

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Guanghua Liu
School of Computer Science, Guangzhou University, Guangzhou, China
Tingting Xu
School of Information Engineering, Hubei University of Science and Technology, Xianning, China
Shenglin Dai
School of Computer and Information, Hefei University, Hefei, China
Peizhi Chen
College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin, China
Yilong Zeng
School of Software and Computer Science, Nanchang Institute of Technology, Nanchang, China

Abstract

This research article explores the development and optimization of large-scale natural disaster prediction models supported by cloud computing technologies. The study emphasizes the integration of advanced computational frameworks to enhance disaster forecasting accuracy and service delivery efficiency. Through a systematic methodology, the paper investigates the role of cloud-based infrastructures in processing vast datasets, enabling real-time predictions, and optimizing resource allocation during emergencies. Results demonstrate significant improvements in prediction reliability and operational scalability, highlighting the transformative potential of cloud computing in disaster management. The discussion addresses challenges such as data security, latency, and computational costs, while proposing strategies for future advancements. This work contributes to the field by offering a robust framework for disaster prediction and service optimization, paving the way for more resilient and responsive systems.

Article Details

Issue

Vol. 2 No. 1 (2026)

Section

Articles

How to Cite

Large-Scale Natural Disaster Prediction Models and Service Optimization Supported by Cloud Computing. (2026). Hua Xia Xin Zhi, 2(1), 291-303. https://journals.hubblepress.com/index.php/HXXZ/article/view/49

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