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Deep Learning for Air Quality Optimization in Urban Smart City Network Systems

Narcisa ZlatanHakan KhanNajaad OubeBlika
Volume 4

Abstract

Urban air pollution significantly impacts public health and environmental sustainability, necessitating intelligent approaches for its prediction and control. This study presents a deep learning-based framework for optimizing air quality in smart city network systems. Utilizing data collected from distributed IoT-enabled environmental sensors, the system employs advanced models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to forecast pollutant concentrations, detect spatial-temporal pollution patterns, and recommend mitigation strategies. These models analyze diverse variables including meteorological conditions, traffic flow, and industrial activities to generate accurate predictions and support proactive urban planning. The proposed system enhances air quality monitoring, facilitates real-time decision-making, and contributes to sustainable urban management. Empirical evaluations demonstrate significant improvements in prediction accuracy and system responsiveness compared to traditional models.

Keywords

Deep learning, air quality, smart city, IoT, LSTM, pollution forecasting

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