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Prediction of Air Quality Index of Sihhiye Region by Extreme Learning Machines and Artificial Neural Networks

Year 2022, Volume: 02 Issue: 01, 1 - 18, 31.07.2022

Abstract

With this study, it was aimed to estimate the air quality index (AQI) in the Sihhiye region with both extreme learning machines (ELM) and artificial neural networks (ANN) algorithms. For this purpose, seven parameters that could affect the AQI had been chosen. These parameters were PM10, SO2, CO, temperature, humidity, pressure and wind speed. Firstly, correlation analysis was performed between the AQI and these seven parameters. According to the results of the analysis, it was concluded that the strongest relation with the AQI were with PM10 from the atmospheric parameters and the pressure from the meteorological parameters. The parameter values for August, October, November and December of 2018 year were determined as training data. The parameter values for the first 14 days of January and February of 2019 year were determined as test data. AQI values were classified mathematically between 1 and 6. Classification studies were applied to both raw data and normalized data. In the classification process, different training functions and hidden neuron numbers were used in algorithms. 3-fold cross-validation was performed for the reliability of the results. The activation function and neuron numbers with highest performance were applied to actual test data. Finally, mathematical classification results were compared with the predicted classification values of AQI. According to the results obtained, in the classification studies conducted with both raw and normalized data, it was observed that ELM algorithm achieved more successful results than ANN algorithm. The success rates were 85.71% in raw data and 71.43% in normalized data.

References

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Sıhhiye Bölgesi Hava Kalitesi İndeksinin Aşırı Öğrenme Makineleri ve Yapay Sinir Ağları ile Tahmini

Year 2022, Volume: 02 Issue: 01, 1 - 18, 31.07.2022

Abstract

Bu çalışma ile Sıhhiye bölgesindeki hava kalitesi indeksinin (HKİ) hem aşırı öğrenme makineleri (AÖM) hem de yapay sinir ağları (YSA) algoritmaları ile tahmin edilmesi amaçlanmıştır. Bu amaçla, HKİ’yi etkileyebilecek yedi adet parametre seçilmiştir. Bu parametreler PM10, SO2, CO, sıcaklık, nem, basınç ve rüzgâr hızıdır. İlk olarak, HKİ ile bu yedi parametre arasında korelasyon analizi yapılmıştır. Analiz sonucuna göre HKİ ile en güçlü ilişkinin atmosferik parametrelerden PM10 ile, meteorolojik parametrelerden ise basınç ile olduğu sonucuna ulaşılmıştır. 2018 yılının Ağustos, Ekim, Kasım ve Aralık aylarına ait parametre değerleri eğitim verisi olarak belirlenmiştir. 2019 yılının Ocak ve Şubat aylarına ait ilk 14 günlük parametre verileri ise test verisi olarak belirlenmiştir. HKİ değerleri 1 ile 6 arasında matematiksel olarak sınıflandırılmıştır. Sınıflandırma çalışmaları hem ham veriler hem de normalize edilmiş veriler ile gerçekleştirilmiştir. Sınıflandırma sürecinde algoritmalarda farklı eğitim fonksiyonları ve gizli nöron sayıları kullanılmıştır. Sonuçların güvenilirliği için 3-kat çapraz doğrulama yapılmıştır. En yüksek performansa sahip aktivasyon fonksiyonları ve nöron sayıları gerçek test verilerine uygulanmıştır. Son olarak, HKİ’nin matematiksel sınıflandırma sonuçları ile tahmini sınıflandırma sonuçları karşılaştırılmıştır. Elde edilen sonuçlara göre hem ham hem de normalize veriler ile yapılan sınıflandırma çalışmalarında AÖM algoritmasının YSA algoritmasından daha başarılı sonuçlar elde ettiği görülmüştür. Başarım oranları ham verilerde %85.71, normalize verilerde %71.43 olarak gerçekleşmiştir.

References

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  • [28] Moustris, K.P., Ziomas, I.C. and Paliatsos, A.G., 2010. 3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece. Water, Air, & Soil Pollution, 209 (1–4), 29–43.
  • [29] Biancofiore, F., Busilacchio, M., Verdecchia, M., Tomassetti, B., Aruffo, E., Bianco, S., Di Tommaso, S., Colangeli, C., Rosatelli, G. and Di Carlo, P., 2017. Recursive neural network model for analysis and forecast of PM10 and PM2.5. Atmospheric Pollution Research, 8, 652–659. https://doi.org/10.1016/j.apr.2016.12.014.
  • [30] Mekparyup, J. and Saithanu, K., 2020. Air Quality Index Prediction in the Eastern Regions of Thailand with Accuracy of Neural Networks. International Journal of Applied Engineering Research, 15 (5), 436-444.
  • [31] Liu, B.C., Binaykia, A., Chang, P.C., Tiwari, M.K. and Tsao, C.C., 2017. Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang. PLoSONE, 12 (7), e0179763. https://doi.org/10.1371/journal.pone.0179763.
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There are 49 citations in total.

Details

Primary Language Turkish
Subjects Artificial Intelligence
Journal Section Research Article
Authors

Burhan Baran 0000-0001-6394-412X

Publication Date July 31, 2022
Published in Issue Year 2022 Volume: 02 Issue: 01

Cite

IEEE B. Baran, “Sıhhiye Bölgesi Hava Kalitesi İndeksinin Aşırı Öğrenme Makineleri ve Yapay Sinir Ağları ile Tahmini”, Researcher, vol. 02, no. 01, pp. 1–18, 2022, doi: 10.55185/researcher.1074394.

The journal "Researcher: Social Sciences Studies" (RSSS), which started its publication life in 2013, continues its activities under the name of "Researcher" as of August 2020, under Ankara Bilim University.
It is an internationally indexed, nationally refereed, scientific and electronic journal that publishes original research articles aiming to contribute to the fields of Engineering and Science in 2021 and beyond.
The journal is published twice a year, except for special issues.
Candidate articles submitted for publication in the journal can be written in Turkish and English. Articles submitted to the journal must not have been previously published in another journal or sent to another journal for publication.