mlsanityData to 7 Oct 2026

Spatio-Temporal Graph Convolutional Networks for Road Network Inundation Status Prediction during Urban Flooding

arXiv 2104.02276 ↗First posted 6 Apr 2021cs.LG physics.soc-phcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 6 of its 55 references that are in the map. First posted on arXiv 6 Apr 2021.

Builds on

6 of 55 references.

References · 6

  1. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  2. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  3. 2016Learning Convolutional Neural Networks for Graphscited by 314
  4. 2020A Network Percolation-based Contagion Model of Flood Propagation and Recession in Urban Road Networkscited by 6
  5. 2020Quantifying Community Resilience Based on Fluctuations in Visits to Point-of-Interest from Digital Trace Datacited by 1
  6. 2021Unveiling Spatial Patterns of Disaster Impacts and Recovery Using Credit Card Transaction Variancescited by 1

Built on by

Cited by year

Citing papers per year: 2021: 3; 2022: 0; 2023: 3; 2024: 2.

8 papers.

  1. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  2. 2021A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Featurescited by 12
  3. 2023Graph Neural Network for spatiotemporal data: methods and applicationscited by 6
  4. 2021Neural Embeddings of Urban Big Data Reveal Emergent Structures in Citiescited by 2
  5. 2024Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networkscited by 2
  6. 2021Predicting Road Flooding Risk with Machine Learning Approaches Using Crowdsourced Reports and Fine-grained Traffic Datacited by 1
  7. 2023MaxFloodCast: Ensemble Machine Learning Model for Predicting Peak Inundation Depth And Decoding Influencing Featurescited by 0
  8. 2024Machine Learning-based Approach for Ex-post Assessment of Community Risk and Resilience Based on Coupled Human-infrastructure Systems Performancecited by 0