Prediction of Air Pollution with Machine Learning Algorithms

Year 2024, Volume: 19 Issue: 1, 1 - 12, 28.03.2024

Kahraman Oğuz Muhammet Ali Pekin

https://doi.org/10.55525/tjst.1224661

Abstract

Air pollution has become an important problem due to its threats. Air pollutants are in complex interaction with atmosphere and environment. For this reason, it is important to study air pollution issues. In recent years, studies on prediction of air pollutants with machine learning methods have gained momentum. In this study, some air pollutants are predicted with various machine learning algorithms considering meteorological factors. In machine learning phase, a separate study is conducted with various machine learning algorithms (multilayer perceptron neural network, stochastic gradient descent, ridge regression, cross decomposition) considering temperature, relative humidity, wind, pressure and air pollutant measurements of previous hour. Consistencies of these algorithms in estimating pollutant concentrations are compared. Various statistical metrics are used to analyze the consistencies. As a result, the coefficient of determination of all algorithms are found above 0.67, considering the test section. It is found that the coefficient of determination of the multilayer perceptron neural network algorithm provides better results than other algorithms.

Keywords

Air pollution, machine learning, neural network, modeling, Çanakkale city

Hava Kirliliğinin Makine Öğrenme Algoritmaları ile Tahmin Edilmesi

Abstract

Hava kirliliği, canlı sağlığına yönelik tehditleri sebebiyle önemli bir problem haline gelmiştir. Hava kirliliği atmosfer ve çevre ile karmaşık ilişki içerisindedir. Bu nedenle hava kirliliği ile alakalı konuların çalışılması önemlidir. Son yıllarda hava kirleticilerinin makine öğrenmesi yöntemleriyle tahmin edilmesine yönelik çalışmalar hız kazanmıştır. Bu çalışmada, meteorolojik faktörler göz önüne alınarak çeşitli makine öğrenme algoritmaları ile bazı hava kirleticilerinin tahmini yapılmıştır. Makine öğrenmesi aşamasında, bir önceki saatin sıcaklık, bağıl nem, rüzgar, basınç ve hava kirletici ölçümleri dikkate alınarak çeşitli makine öğrenmesi algoritmaları (çok katmanlı algılayıcı sinir ağı, stokastik gradyan inişi, sırt regresyonu, çapraz ayrıştırma) ile ayrı ayrı çalışma yapılmıştır. Bu algoritmaların kirletici konsantrasyonlarını tahmin etmedeki tutarlılıkları karşılaştırılmıştır. Tutarlılıkları analiz etmek için çeşitli istatistiksel metrikler kullanılmıştır. Sonuç olarak, test bölümü dikkate alındığında tüm algoritmaların belirleme katsayısı 0.67'nin üzerinde bulunmuştur. Çok katmanlı algılayıcı sinir ağı algoritmasının belirleme katsayısının diğer algoritmalara göre daha iyi sonuçlar verdiği tespit edilmiştir.

Keywords

Hava kirliliği, makine öğrenmesi, sinir ağı, modelleme, Çanakkale ili

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