A Graph Neural Network for Global Daily Fire Radiative Power Prediction at Medium-Range Lead Times
The model is a lightweight graph neural network. It treats each spatial patch as a grid graph, applies residual graph convolutions to each day of input history, and then aggregates the history with temporal convolutions. A two-part (hurdle) output head predicts both the probability of occurrence and the conditional int...