Air Quality Forecasting in Urban Environments: A Deep Learning Approach

Yasin Kırelli

doi:10.29130/dubited.1591784

TR EN

Kentsel Bölgelerde Hava Kalitesinin Öngörüsü: Derin Öğrenme Yaklaşımı

Abstract

Hava kirliliği çevresel ve insan sağlığı üzerindeki etkileri nedeniyle önemli bir araştırma konusu haline gelmiştir. Günümüzde hızlı sanayileşme ve kentleşme nedeniyle hava kalitesi kötüleşmekte olup zararlı gazların emisyonunda önemli faktörlerden biridir. Bu çalışmada hava kalitesi sorunları ele alınmış ve kükürt dioksit (SO2), azot dioksit (NO2), karbon monoksit (CO) ve partikül madde (PM2.5 ve PM10) gibi kirleticilerin insan sağlığı üzerindeki olumsuz etkileri ve sonuçları değerlendirilmiştir. Bu çalışmada İstanbul Beşiktaş'taki hava kalitesi verileri hava kirletici seviyelerini ve değerlerini tahmin etmek için Evrişimsel Sinir Ağları (CNN), Uzun Kısa Süreli Bellek (LSTM) ve Geçitli Yinelemeli Birim (GRU) tabanlı derin öğrenme modelleri kullanılarak analiz edilmiştir. Bu modellerin performansı, Ortalama Karesel Hata (MSE), Ortalama Mutlak Hata (MAE), Kök Ortalama Karesel Hata (RMSE) ve Ortalama Mutlak Yüzdelik Hata (MAPE) gibi metrikler kullanılarak değerlendirilmiştir. Çalışmanın bulguları sunulan GRU modelinin CO ve NO2 gibi kirleticiler için üstün tahmin doğruluğu sağladığını, CNN modelinin ise SO2 ve O3 tahminleri için daha iyi performans gösterdiğini ortaya koymaktadır. Bu çalışma hava kirliliği yönetimi için gelişmiş derin öğrenme tekniklerinin kullanılmasının önemini vurgulamaktadır. Sürdürülebilir kalkınma için politika yapma sürecine katkıda bulunmak üzere öngörücü modellerin potansiyelini sunmaktadır.

Keywords

Hava kalitesi tahmini, Derin öğrenme modelleri, Kirlilik öngörüsü

Air Quality Forecasting in Urban Environments: A Deep Learning Approach

Abstract

Air pollution has become an important research topic due to its environmental and human health effects. Today, rapid industrialization and urbanization is one of the major factors in the emission of harmful gases, leading to deteriorating air quality. In this study, air quality problems are discussed, and the adverse effects and consequences of pollutants including sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM2.5 and PM10) on human health are assessed. In this study, air quality data from Beşiktaş, Istanbul, has been analyzed by using deep learning models based on Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) to predict air pollutant levels and values. The performance of these models is evaluated using metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The study's findings reveal that the presented GRU model provides superior forecast accuracy for pollutants like CO and NO2, while the CNN model performs better for SO2 and O3 forecasts. This study highlights the importance of using advanced deep-learning techniques for air pollution management. It shows the potential of predictive models to contribute to the policy-making process for sustainable development.

Keywords

Air Quality Forecasting, Deep Learning Models, Urban Environments, Pollution Forecasting

Ethical Statement

This study does not involve human or animal participants. All procedures followed scientific and ethical principles, and all referenced studies are appropriately cited.

Thanks

The author would like to express their sincere thanks to the editor and the anonymous reviewers for their helpful comments and suggestions.

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Details

Primary Language

English

Subjects

Deep Learning, Neural Networks, Machine Learning Algorithms

Journal Section

Research Article

Authors

Yasin Kırelli ^*
0000-0002-3605-8621
Türkiye

Publication Date

October 30, 2025

Submission Date

November 27, 2024

Acceptance Date

May 18, 2025

Published in Issue

Year 2025 Volume: 13 Number: 4

DOI

https://doi.org/10.29130/dubited.1591784

IZ

https://izlik.org/JA62AM59DF

APA

Kırelli, Y. (2025). Air Quality Forecasting in Urban Environments: A Deep Learning Approach. Duzce University Journal of Science and Technology, 13(4), 1445-1454. https://doi.org/10.29130/dubited.1591784

AMA

1.Kırelli Y. Air Quality Forecasting in Urban Environments: A Deep Learning Approach. DUBİTED. 2025;13(4):1445-1454. doi:10.29130/dubited.1591784

Chicago

Kırelli, Yasin. 2025. “Air Quality Forecasting in Urban Environments: A Deep Learning Approach”. Duzce University Journal of Science and Technology 13 (4): 1445-54. https://doi.org/10.29130/dubited.1591784.

EndNote

Kırelli Y (October 1, 2025) Air Quality Forecasting in Urban Environments: A Deep Learning Approach. Duzce University Journal of Science and Technology 13 4 1445–1454.

IEEE

[1]Y. Kırelli, “Air Quality Forecasting in Urban Environments: A Deep Learning Approach”, DUBİTED, vol. 13, no. 4, pp. 1445–1454, Oct. 2025, doi: 10.29130/dubited.1591784.

ISNAD

Kırelli, Yasin. “Air Quality Forecasting in Urban Environments: A Deep Learning Approach”. Duzce University Journal of Science and Technology 13/4 (October 1, 2025): 1445-1454. https://doi.org/10.29130/dubited.1591784.

JAMA

1.Kırelli Y. Air Quality Forecasting in Urban Environments: A Deep Learning Approach. DUBİTED. 2025;13:1445–1454.

MLA

Kırelli, Yasin. “Air Quality Forecasting in Urban Environments: A Deep Learning Approach”. Duzce University Journal of Science and Technology, vol. 13, no. 4, Oct. 2025, pp. 1445-54, doi:10.29130/dubited.1591784.

Vancouver

1.Yasin Kırelli. Air Quality Forecasting in Urban Environments: A Deep Learning Approach. DUBİTED. 2025 Oct. 1;13(4):1445-54. doi:10.29130/dubited.1591784

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