Enhancing Graph Neural Networks Using Proximity Graphs for Dust Source Emission Forecasting
Researchers have developed a novel method to enhance Graph Neural Networks (GNNs) for dust source emission forecasting by incorporating proximity graphs. These graphs, including Delaunay triangulation, Gabriel graph, k-Nearest Neighbor graph, and Yao graph, are used as input for various GNN architectures like GraphSAGE, Graph Convolutional Networks, and Graph Attention Networks. The study demonstrates that GNNs utilizing proximity graphs significantly outperform those using random graphs and are also superior to traditional Long Short-Term Memory (LSTM) models in accurately predicting dust storm phenomena. AI
IMPACT This research could lead to more accurate environmental forecasting models, improving disaster preparedness and public health.