Description

This Master Prompt investigates forecasting methodologies for systems in which future outcomes depend on both temporal dynamics and spatial relationships across geographical locations, networks, physical environments, or interconnected infrastructures. It encompasses spatial statistics, geostatistical forecasting, spatiotemporal regression, spatial autocorrelation, graph-based forecasting, graph neural networks, graph attention mechanisms, spatiotemporal transformers, convolutional–recurrent architectures, and multiregional forecasting frameworks. The theme addresses spatial heterogeneity, dynamic spatial dependence, sparse observations, missing measurements, geographical transferability, cross-location generalisation, spatial interpolation, remote sensing, meteorological data assimilation, and multiscale spatial representation learning. It also examines physics-informed neural networks, physical constraints, numerical simulation integration, conservation principles, and hybrid data-driven–physics-based forecasting. Applications include weather and climate forecasting, wind and solar power, hydrology, flooding, ocean conditions, air pollution, agriculture, transportation, and distributed energy systems.