Application of Machine Learning and Deep Learning in Weather-related Forecasting

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.