With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …

Short‐term forecasting of water demand is a crucial process for managing efficiently water
supply systems. This paper proposes to develop a novel graph convolutional recurrent …

Accurately predicting the magnitude and timing of floods is an extremely challenging
problem for watershed management, as it aims to provide early warning and save lives …

Being a kind of non-Euclidean data, spatiotemporal graph data exists everywhere from traffic
flow, air quality index to crime case, etc. Unlike the raster data, the irregular and disordered …

Physics-based models are computationally time-consuming and infeasible for real-time
scenarios of urban drainage networks, and a surrogate model is needed to accelerate the …

Flood nowcasting refers to near-future prediction of flood status as an extreme weather
event unfolds to enhance situational awareness. The objective of this study was to adopt …

Urban flooding disrupts traffic networks, affecting mobility and disrupting residents' access.
Flooding events are predicted to increase due to climate change; therefore, understanding …

Extreme events disrupt routine mobility patterns and affect citizens' daily lives. Current
disaster resilience measurements often rely on static proxies or topological analyses, failing …

The objective of this research is to explore the temporal importance of community-scale
human activity features for rapid assessment of flood impacts. Ultimate flood impact data …

Timely, accurate, and reliable information is essential for decision-makers, emergency
managers, and infrastructure operators during flood events. This study demonstrates that a …