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What is Machine Learning for Ozone Prediction about?

This study evaluates nineteen machine learning models for predicting ozone pollution, emphasizing the importance of incorporating time-lagged data and relevant environmental features to enhance prediction accuracy. Results indicate that dynamic models using time-lagged data significantly outperform static models, improving accuracy by up to 300%. The findings underscore the potential of machine learning approaches in effectively forecasting ozone levels, which is crucial for environmental policy and public

Author

Miraceli Bonjardim

Language

EN