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Application of Machine Learning and Deep Learning in Weather-related Forecasting
- 1 Computer Science, Hainan Bielefeld University of Applied Sciences, Danzhou, Hainan, China
* Author to whom correspondence should be addressed.
Abstract
Weather plays a broad and decisive role in many areas. Its volatility can disrupt traffic and endanger lives. It is therefore imperative to accurately predict its impact. Improved forecasting accuracy can aid multi-industry decision making. Traffic authorities can control traffic in advance; Agriculture can adjust its strategy in time; Resource allocation can be optimized in the energy sector. The rise of machine learning and deep learning technologies has opened up new prospects for weather-related forecasting. This article takes a methodical look at the application of machine learning and deep learning to traffic and weather forecasting, dissecting the details of model construction, data processing processes, and performance evaluation metrics. By comparing the advantages and disadvantages of each model, it provides ideas for model improvement, powerfully guides future research direction, and lays the foundation for building a more accurate prediction framework. The research direction of this thesis has far-reaching theoretical and practical value.
Keywords
Machine learning, deep learning, weather prediction, model application
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Cite this article
Ye,Z. (2025). Application of Machine Learning and Deep Learning in Weather-related Forecasting. Applied and Computational Engineering,140,38-42.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Mechatronics and Smart Systems
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