Post-processing of air pollutant predictions using machine learningMajkić, Dragan (Avtor) Faganeli Pucer, Jana (Mentor) environmental data sciencemachine learningtime seriesair pollutionpost-processingAir pollution is a critical environmental and public health problem, making accurate air pollutant forecasting important for decision-makers and the general public. Numerical air quality models such as CAMx provide operational forecasts, but their predictions can contain errors and therefore require corrections (post-processing). This thesis investigates machine learning and deep learning methods for post-processing CAMx air pollutant forecasts in Slovenia. The developed approach combines CAMx air quality forecasts, ALADIN meteorological forecasts, and observations from Slovenian monitoring stations. We compared baseline, machine learning, and deep learning methods using an expanding window walk-forward evaluation. The analysis focused on hourly PM10 and O3 concentrations and forecasting exceedances of limit values. All post-processing methods improved the original CAMx forecasts. XGBoost and the Temporal Fusion Transformer (TFT) achieved the best results, with TFT reducing RMSE by approximately 36% for hourly PM10 and 51% for hourly O3. Post-processing also improved exceedance detection. Overall, the results show that the proposed methodology can improve CAMx forecasts and provide more reliable next-day hourly predictions.20262026-08-26 12:00:08Magistrsko delo/naloga186066sl