Spatio-Temporal Graph Convolutional Networks for Road Network Inundation Status Prediction during Urban Flooding
This work addresses a critical gap in predictive flood monitoring for road networks, benefiting affected communities and emergency management agencies by enabling situational awareness and crisis response planning, though it is incremental as it applies existing STGCN methods to a new domain-specific problem.
This study tackled the problem of predicting near-future flooding status of road segments during urban flooding using spatio-temporal graph convolutional networks (STGCN) on fine-grained traffic data from Hurricane Harvey, achieving precision and recall values larger than 98% and 96%, respectively, for reliable predictions up to 2-4 hours ahead.
The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments current status through the use of deep learning framework on fine-grained traffic data. Predictive flood monitoring for situational awareness of road network status plays a critical role to support crisis response activities such as evaluation of the loss of access to hospitals and shelters. Existing studies related to near-future prediction of road network flooding status at road segment level are missing. Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during flood events at the road segment level in the context of the 2017 Hurricane Harvey in Harris County (Texas, USA). Model 1 consists of two spatio-temporal blocks considering the adjacency and distance between road segments, while Model 2 contains an additional elevation block to account for elevation difference between road segments. Model 3 includes three blocks for considering the adjacency and the product of distance and elevation difference between road segments. The analysis tested the STGCN models and evaluated their prediction performance. Our results indicated that Model 1 and Model 2 have reliable and accurate performance for predicting road network flooding status in near future (e.g., 2-4 hours) with model precision and recall values larger than 98% and 96%, respectively. With reliable road network status predictions in floods, the proposed model can benefit affected communities to avoid flooded roads and the emergency management agencies to implement evacuation and relief resource delivery plans.