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What is PM Prediction in Seoul Using ML Techniques about?

This study presents a method for short-term prediction of particulate matter (PM10 and PM2.5) in Seoul, South Korea, using tree-based machine learning algorithms, specifically the light gradient boosting (LGB) algorithm. The research utilized meteorological data from the local data assimilation and prediction system (LDAPS) and demonstrated that the LGB algorithm outperformed traditional chemical transport models in terms of prediction accuracy. Results indicated significant improvements in PM prediction pe

Author

Le DuyLam Kyd

Language

EN