Probabilistic Forecasting Prediction Model for Anticipating Days with Elevated Air Pollution

Public health and the environment are both threatened by air pollution. For prompt responses and public awareness, accurate air pollution forecasting is essential, especially on days with high ozone levels. With a focus on ground ozone concentrations, the objective of this study is to develop a probabilistic forecasting model in Python. Using machine-learning algorithms and meteorological information like wind speed and outdoor temperature, the possibility of an ozone day is predicted. Python packages like Pandas, NumPy, and Scikit-Learn are used for implementation and assessment. The results demonstrate the effectiveness of the developed probabilistic forecasting models, with significant improvements over baseline configurations. The most promising configuration is found, demonstrating the power of ensemble approaches and parameter fine-tuning. The Brier skill score of the gradient boosting method shows the best results. The ensemble model’s performance is evaluated using Brier skill scores and compared to a naive prediction model. According to the results the gradient-boosting ensemble model has the best BSS of 0.119546. The importance of probabilistic forecasting in handling the complexity of air pollution prediction is emphasized. This research develops probabilistic forecasting approaches for air pollution prediction, which improves both environmental management and public health. Despite the challenges faced by the complexity of air pollution, the findings help enhance air pollution prediction.