Advances in spatiotemporal graph neural network prediction research

Data-Driven Wildfire Spread Modeling of European Wildfires Using a Spatiotemporal Graph Neural Network

“…STGNNs can solve complex spatiotemporal modeling problems by combining a spatial modeling component (e.g., GCN) with a temporal modeling component (e.g., RNN) to simultaneously learn multidimensional patterns from data [21]. This has resulted in superior results compared to traditional DL architectures in a variety of research fields [36]. In theory, this architecture can be applied to the complex and non-linear wildfire spread process, where multiple fire driver variables interact along different temporal and spatial scales [10,13,55].…”

“…Within the traffic forecasting domain, spatial convolutions achieved superior results over spectral convolutions [35], whereas the latter was implemented in the used TGCN [58]. Also, attention-based GNNs showed improved performances compared to GCN-based methods, but mostly for long-term forecasting problems [35,36]. As this work features the first usage of STGNNs for the application of wildfire spread modeling, no significant statements about performance-enhancing STGNN architectures for this specific modeling problem can be given.…”

“…For this reason, the well-known Temporal Graph Convolutional Network (TGCN) architecture developed by Zhao et al [58] was selected to be applied to the wildfire spread modeling problem. The TGCN can model spatiotemporal dependencies using multivariate features and showed strong performances in predicting traffic flows [36]. Figure 4a describes the TGCN architecture schematically.…”

“…STGNNs implement a spatial (e.g., a Graph Convolutional Network (GCN)) and a temporal modeling component (e.g., an RNN) to simultaneously learn spatial and temporal data dependencies within a graph [21]. This allows for modeling spatiotemporal systems with high accuracy and efficiency [21] and results in improved results in various domains, e.g., traffic forecasting, recommendation systems, or social network analysis [35,36]. The application of STGNNs for modeling natural hazards is, however, still limited, with only a few studies focusing on the prediction of urban floodings [37], typhoon intensity [38], or the location and magnitude of earthquakes [39].…”