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Transforming Air Quality Forecasting Using Advanced 1D Deep Learning Models
SHUBHI SAXENA Prof (Mr.) Mohd. Arif
Abstract:
Air quality forecasting plays a crucial role in environmental management and public health. Traditional methods often struggle to accurately predict pollutant concentrations due to complex interactions of meteorological factors and emissions sources. Advanced 1D deep learning models, including convolutional and recurrent neural networks, have emerged as promising tools for improving forecasting accuracy. These models excel in capturing intricate temporal and spatial patterns in air quality data, offering advantages over conventional statistical approaches. This review explores recent advancements in 1D deep learning techniques applied to air quality forecasting, highlighting methodologies, case studies, and challenges. It aims to provide insights into the potential of deep learning models to transform air quality prediction, guiding future research and applications in environmental science and policy.