Comparison of Machine Learning and Deep Learning Methods for Modeling Ozone Concentrations

Year 2022, Volume: 5 Issue: 2, 106 - 118, 21.09.2022

Şevket Ay , Ekin Ekinci

https://doi.org/10.38016/jista.1054331

Abstract

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.

Keywords

Machine learning, Deep learning, Extreme Gradient Boosting (Xgboost), Artificial Neural Network (ANN), Long-Short Term Memory (LSTM), Time Series

Ozon Konsantrasyonlarını Modellemek için Makine Öğrenmesi ve Derin Öğrenme Yöntemlerinin Karşılaştırılması

Abstract

Hava kirliliği günümüz için önemli bir problem olmakla birlikte sanayileşme, orman yangınları, egzoz gazları, kalitesiz yakıt kullanımı gibi sebepler gelecek nesilleri de tehdit edecek ciddi bir problem ile bizleri yüzleştirmektedir. Bu sebepler içerisinde ise yoğun sanayileşme hava kirliliğinde rol oynayan en önemli faktörlerden birisidir. Bölgesel sanayi gelişimi şehirlerde hava kalitesini etkilemektedir. Sanayinin gelişmesi ile birlikte bazı kirleticilerin miktarı azalmakta iken, ozon seviyelerinde artış yaşanmaktadır. Önümüzdeki yıllarda hava kirliliğini neden olacağı problemleri daha fazla hissetmemek, hava kalitesini yönetmek ve risklere karşı önlem almak için hava kirliliğinin tahmin edilmesi kaçınılmaz hale gelmektedir. Bu çalışmada sanayinin gelişmiş olduğu Kocaeli ve Sakarya illeri ile sanayinin çok fazla gelişmediği Çanakkale illeri için 2018-2021 arası saatlik ozon seviyelerini tahmin etmek amacıyla zaman serilerine dayalı makine öğrenmesi ve derin öğrenme yöntemleri uygulanmıştır. Uygulanan modeller Ortalama Mutlak Hata (MAE), Bağıl Mutlak Hata (RAE) ve R-kare (R2) metrikleri kullanılarak karşılaştırılmış ve en etkin yöntemin belirlenmesi amaçlanmıştır.

Keywords

Makine Öğrenmesi, Derin Öğrenme, Aşırı Gradyan Arttırma (Xgboost), Yapay Sinir Ağları (YSA), Uzun Kısa Süreli Bellek (LSTM), Zaman Serileri

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