Yapay Zeka ve IoT Kullanarak İzmir'deki Hava Kalitesinin Tahmini

Year 2025, Volume: 6 Issue: 2, 341 - 364, 30.09.2025

Kübra Öztürk , Zuhal Can

https://doi.org/10.53710/jcode.1536480

Abstract

Hava kirliliği, Türkiye'nin üçüncü büyük şehri olan İzmir'de önemli bir endişe kaynağıdır ve insan sağlığı ve çevre üzerinde ciddi olumsuz etkileri vardır. Şehir, endüstriyel faaliyetler ve yoğun trafik nedeniyle hava kalitesi sorunlarıyla karşı karşıyadır. Nesnelerin İnterneti (IoT) teknolojisi, hava kalitesini düşüren faktörlerin sürekli izlenmesini ve ölçülmesini sağlar. Hava kirliliğini tahmin etmek için IoT'den yararlanmak, olası olumsuz etkileri azaltmada çok önemlidir. Bu çalışmada, hava kirliliği tahminleri makine öğrenimi, derin öğrenme ve zaman serisi analiz yöntemleri kullanılarak gerçekleştirilmiştir. PM10 ve SO2 seviyelerine ilişkin veriler, 1996'dan 2024'e kadar İzmir'deki yedi lokasyondan toplandı. PM10 ve SO2 ölçümlerini değerlendirmek için kullanılan modeller Destek Vektör Regresyonu (SVR), Mevsimsel Otoregresif Entegre Hareketli Ortalama (SARIMA), Uzun Kısa Süreli Bellek (LSTM) ve Genişletilmiş Uzun Süreli Bellek'i (xLSTM) içeriyordu. Bu modeller arasında xLSTM, LSTM modeline göre biraz daha düşük R² puanlarına rağmen en düşük hata ölçütlerini elde ederek hem PM10 hem de SO2 seviyelerini tahmin etmede genel olarak en iyi performansı göstermiştir.

Keywords

Hava Kalitesi , Yapay Zeka , Nesnelerin İnterneti , Hava Tahmini , xLSTM

Predicting Air Quality in Izmir Using Artificial Intelligence and IoT

Abstract

ir pollution is a significant concern in Izmir, the third-largest city in Turkey, and it has serious adverse effects on human health and the environment. The city faces air quality issues due to industrial activities and heavy traffic. The Internet of Things (IoT) technology enables continuous monitoring and measurement of factors that diminish air quality. Utilizing IoT to predict air pollution is crucial in mitigating potential adverse effects. In this study, air pollution predictions were conducted using machine learning, deep learning, and time series analysis methods. Data on PM10 and SO2 levels were collected from seven locations in Izmir from 1996 to 2024. The models used for evaluating PM10 and SO2 measurements included Support Vector Regression (SVR), Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and Extended Long-Term Memory (xLSTM). Among these models, xLSTM demonstrated the best overall performance for predicting both PM10 and SO2 levels, achieving the lowest error metrics despite slightly lower R² scores than the LSTM model.

Keywords

Air Quality , Artificial Intelligence , IoT , Weather Forecasting , xLSTM

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