TY  - JOUR
T1  - Air Quality Forecasting in Urban Environments: A Deep Learning Approach
TT  - Kentsel Bölgelerde Hava Kalitesinin Öngörüsü: Derin Öğrenme Yaklaşımı
AU  - Kırelli, Yasin
PY  - 2025
DA  - October
Y2  - 2025
DO  - 10.29130/dubited.1591784
JF  - Duzce University Journal of Science and Technology
JO  - DÜBİTED
PB  - Duzce University
WT  - DergiPark
SN  - 2148-2446
SP  - 1445
EP  - 1454
VL  - 13
IS  - 4
LA  - en
AB  - 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.
KW  - Air Quality Forecasting
KW  - Deep Learning Models
KW  - Urban Environments
KW  - Pollution Forecasting
N2  - 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.
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UR  - https://doi.org/10.29130/dubited.1591784
L1  - https://dergipark.org.tr/en/download/article-file/4395872
ER  -