Machine Learning for Urban Air Quality Forecasting
DOI:
https://doi.org/10.63345/sjaibt.v2.i3.204Keywords:
Machine Learning, Air Quality Forecasting, Urban Pollution, Deep Learning, Predictive Modeling, Smart CitiesAbstract
Air quality degradation in urban areas poses severe threats to public health, economic development, and environmental sustainability. Traditional statistical and deterministic models for air quality forecasting often fail to capture the highly nonlinear, dynamic, and spatiotemporal characteristics of urban pollution. In recent years, Machine Learning (ML) has emerged as a transformative paradigm, leveraging diverse datasets—from meteorological conditions and traffic patterns to satellite imagery and IoT-based sensors—to deliver more accurate, scalable, and adaptive forecasting solutions.
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