Performance Evaluation of Various Regression Models and Features for Prediction of Ozone Concentration

Yıl 2020, Cilt: 35 Sayı: 3, 567 - 574, 30.09.2020

Sezer Dümen Ercan Avşar Ulus Çevik

https://doi.org/10.21605/cukurovaummfd.845985

Öz

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.

Anahtar Kelimeler

Air quality, Ozone concentration, Machine learning, Regression

Ozon Konsantrasyonu Tahmininde Çeşitli Regresyon Modelleri ve Özniteliklerin Performans Değerlendirmesi

Öz

Ozondan kaynaklanan hava kirliliği insan sağlığını tehdit eden bir problemdir. Bu nedenle, O3 konsantrasyonunun tahmini önemlidir. Bu çalışmada, Türkiye’nin Adana ili için O3 konsantrasyon seviyesi, destek vektör regresyonu (DVR), çok katmanlı algılayıcı (ÇKA), gradyan artırılmış karar ağaçları (GAKA), K en yakın komşu (KEK) ve elastik net makinesi öğrenme yöntemleri kullanılarak tahmin edilmiştir. Bu tahmin için kullanılan parametreler, partiküler madde (PM10), sülfür dioksit (SO2), azot dioksit (NO2), azot oksitler (NOx), azot monoksit (NO) gibi kirleticilerin konsantrasyonları ve ayrıca hava sıcaklığı, rüzgâr hızı, bağıl nem, hava basıncı, rüzgâr yönü gibi meteorolojik parametrelerin saatlik ölçümleridir. Ek olarak saat, gün ve sezon bilgileri de parametre olarak kullanılmaktadır. DVR yöntemi ile elde edilen R2 değeri 0,9697 olup diğer yöntemlerle elde edilen değerlerden yüksektir. Ayrıca öznitelik seçimi için geriye doğru eleme yöntemi uygulanmıştır ve sonuçlara göre bir sonraki saatin O3 konsantrasyonunu tahmin etmek için şimdiki O3 konsantrasyon seviyesinin en önemli öznitelik olduğu görülmüştür.

Anahtar Kelimeler

Hava kalitesi, Ozon konsantrasyonu, Makine öğrenmesi, Regresyon

Kaynakça

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