A Hybrid Deep Learning and Fuzzy Logic Framework for PM10 Concentration Forecasting in Istanbul

 

 

 

Rusul Al-bayati1,*, Ülkü Alver Şahin2, Hüseyin Toros1

 

1Istanbul Technical University Faculty of Aeronautics and Astronautics, Department of Climate Science and Meteorological Engineering Istanbul, Türkiye

 

2Department of Environmental Engineering, Engineering Faculty, Istanbul University-Cerrahpaşa, Avcılar, Istanbul, Turkey

 

Text Box: Abstract Air pollution, especially atmospheric particulate matter with aerodynamic diameters smaller than 10 micrometers (PM10), is one of the constant and serious environmental challenges in urban areas. Its consequences range from negative human health effects to broader ecological disruptions. With the increasing necessity of accurate and trustworthy forecasting devices in the sphere of air quality assessment, we propose a new hybrid-modeling platform that merges the sequential pattern recognition ability of Long Short Term Memory (LSTM) neural networks with fuzzy logic reasoning. The two approaches implemented in this model complement each other: while approaches taking into account the time dependence of the behavior of air pollutants address the complex temporal dynamics present in the problem, methods based on uncertainty propagate inherent uncertainties in the meteorological and environmental data. The model was trained using a well-structured, multi-variable dataset of hourly air quality and meteorological observations for five years (2019–2023) measured in Istanbul and further tested of January 2024 data. The hybrid approach outperformed all tested environments in prediction output, reaching an accuracy of 98% at the Aksaray traffic station, whereas standalone LSTM (97%) and fuzzy logic (94%) models performed lower. Importantly, it identified minute periodicity and pollution peaks with high fidelity and demonstrated robustness across diverse settings such as traffic-dense, industrial, rural and urban zones. These results place the hybrid LSTM–Fuzzy Logic model as a trusted and robust forecasting tool for predicting PM10 concentrations, providing valuable assistance to environmental policy-makers, urban planners, and public health authorities in efforts to reduce air pollution and protect the health of the population. Emails: al-bayati20@itu.edu.tr; ulkualver@iuc.edu.tr; toros@itu.edu.tr

 

Received: April 09, 2025 Revised: June 12, 2025 Accepted: August 10, 2025

 

Keywords: Air pollution; Long Short-term Memory (LSTM); Fuzzy Logic; Hybrid mode; Machine learning; Environmental modeling