Ozon Konsantrasyonlarını Modellemek için Makine Öğrenmesi ve Derin Öğrenme Yöntemlerinin Karşılaştırılması.

Although air pollution is an important problem for today, reasons such as industrialization, forest fires, exhaust gases, poor quality fuel use confront us with a serious problem that will threaten future generations. Among these reasons, intensive industrialization is one of the most critical factors that play a role in air pollution. Regional industrial development affects air quality in cities. While the amount of some pollutants decreases with the development of the industry, there is an increase in ozone levels. In the coming years, it becomes inevitable to predict air pollution in order not to feel the problems that air pollution will cause more, to manage air quality, and to take precautions against risks. In this study, machine learning and deep learning methods based on time series were applied to predict hourly ozone levels between 2018 and 2021 for the provinces of Kocaeli and Sakarya, where the industry is developed, and Çanakkale, where the industry is not developed much. The applied models were compared using Mean Absolute Error (MAE), Relative Absolute Error (RAE), and R-square (R2) metrics, and it was aimed to determine the most effective method. [ABSTRACT FROM AUTHOR]