MVG-KAN: Multi-View Geo-Wind Guided KAN for PM$_{2.5}$ Forecasting
Accurate short-term PM$_{2.5}$ forecasting is important for public health protection, air-quality early warning, and urban environmental management. However, PM$_{2.5}$ variation is driven by multiple coupled factors, including stable periodic changes induced by human activities and meteorological regularity, station-specific short-term concentration evolution, and meteorology-driven pollutant dispersion among monitoring stations. Existing spatio-temporal forecasting methods may capture station relationships to some extent, but distance-only, correlation-based, or purely adaptive graphs are often insufficient to comprehensively represent these heterogeneous factors, especially wind-direct...
| Authors | Cheng Huang, Muyao Guan, Jairus Yougui Railey et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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