Burhan BARAN

4617

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

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.
Anahtar Kelimeler:

Hava kalitesi indeksi, Aşırı öğrenme makineleri, Yapay sinir ağları, Sınıflandırma, Korelasyon.

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

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.
Keywords:

Air quality index, Extreme learning machine, Artificial neural networks, Classification, Correlation.,

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Researcher-Cover
  • ISSN: 2717-9494
  • Yayın Aralığı: Yılda 2 Sayı
  • Başlangıç: 2013
  • Yayıncı: Ankara Bilim Üniversitesi
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