Özet:

Air pollution caused by ozone is a problem which threaten human health. Therefore, prediction of O3 concentration is important. In this work, O3 concentration level for Adana, Turkey is predicted with support vector regression (SVR), multi-layer perceptron (MLP), gradient boosting decision trees (GBDT), K nearest neighbors (KNN), elastic net machine learning methods. Parameters utilized for this prediction are hourly measurement of pollutants like particular matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), nitrogen oxides (NOx), nitric oxide (NO) concentrations and also meteorological parameters like air temperature, wind speed, relative humidity, air pressure, wind direction. Additionally, hour, day and season information are used as features. It has been shown that SVR method achieves the best result with R2 value of 0.9697. Furthermore, backward elimination method is implemented for feature selection process and according to the results, current O3 concentration has the highest importance to predict the concentration for the next hour.

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