AGATNet: An Adaptive Graph Attention Network for Bias Correction of CMAQ‐Forecasted PM2.5 Concentrations Over South Korea
Rijul Dimri,
Yunsoo Choi,
Ahmed Khan Salman
et al.
Abstract:Accurate forecasting of surface PM2.5 concentrations is essential for enhancing air quality insights and enabling informed decision‐making in a timely manner. Traditional numerical models often exhibit biases originating from uncertainties in input parameters and oversimplified parameterization. This study introduces AGATNet, a graph‐based neural network aimed at correcting such biases by adaptively learning the spatial connections between air quality monitoring stations and associated temporal dependency of i… Show more
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