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Multimodal fog visibility classification with transformer-based feature encoding and LightGBM

algorithms-and-analysis-of-algorithms
artificial-intelligence
data-mining-and-machine-learning
data-science
neural-networks

Abstract

The complex mechanisms underlying fog formation present significant challenges for accurate prediction. However, precise fog forecasting is critical for ensuring the safety of maritime, aviation, and land transportation, particularly with the ongoing development of the Hainan Free Trade Port. This study examines real-time meteorological data from five stations on Hainan Island in conjunction with numerical predictions from the European Center for Medium-Range Weather Forecasts (ECMWF). Utilizing ensemble learning, we introduce an innovative dense fog prediction model, Transformer–LightGBM (T-LGB).. The results demonstrate that the T-LGB model achieves a classification accuracy of 96.27%, surpassing the performance of alternative algorithms. This research provides a practical fog prediction framework specifically tailored to the Hainan Free Trade Port, thereby enhancing transportation safety and efficiency. Furthermore, it evaluates the effectiveness of integrated learning approaches for multi-modal data in meteorological research.
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