OmniPMNet: Bridging discrete and gridded PM10 forecasts via omni-query neural processes
OmniPM-Net fuses chemical transport models and graph neural networks to improve PM10 air quality forecasting.
OmniPM-Net uses a Convolutional Conditional Neural Process to reconcile gridded chemical transport model outputs with discrete station-based graph neural network data. By employing a terrain-aware Gaussian set convolution, the model creates a unified spatial representation that improves accuracy for both local monitoring sites and broader regional fields during dust events.
