Akademik Veri Yönetim Sistemi
Journal of Intelligent Systems and Internet of Things, cilt.18, sa.2, ss.142-156, 2026 (Scopus)
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.