# A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Features

arXiv: https://arxiv.org/abs/2111.08450 · HTML: https://mlsanity.com/paper/2111.08450/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 92 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 15 of 15)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach](https://mlsanity.com/paper/1712.04997/) (2017)
- [AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/2011.11004/) (2020)
- [Edge Attention-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1802.04944/) (2018)
- [A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data](https://mlsanity.com/paper/2006.09201/) (2020)
- [Twitter Speaks: A Case of National Disaster Situational Awareness](https://mlsanity.com/paper/1903.02706/) (2019)
- [Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Results](https://mlsanity.com/paper/2010.07474/) (2020)
- [Spatio-Temporal Graph Convolutional Networks for Road Network Inundation Status Prediction during Urban Flooding](https://mlsanity.com/paper/2104.02276/) (2021)
- [A Network Percolation-based Contagion Model of Flood Propagation and Recession in Urban Road Networks](https://mlsanity.com/paper/2004.03552/) (2020)
- [Physics Informed Data Driven model for Flood Prediction: Application of Deep Learning in prediction of urban flood development](https://mlsanity.com/paper/1908.10312/) (2019)
- [Decentralized Flood Forecasting Using Deep Neural Networks](https://mlsanity.com/paper/1902.02308/) (2019)
- [Unraveling the Temporal Importance of Community-scale Human Activity Features for Rapid Assessment of Flood Impacts](https://mlsanity.com/paper/2106.08370/) (2021)

## Built on by (most cited first, 13 of 13)

- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [Graph Neural Network for spatiotemporal data: methods and applications](https://mlsanity.com/paper/2306.00012/) (2023)
- [FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation](https://mlsanity.com/paper/2405.14232/) (2024)
- [FloodLense: A Framework for ChatGPT-based Real-time Flood Detection](https://mlsanity.com/paper/2401.15501/) (2024)
- [Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks](https://mlsanity.com/paper/2404.10324/) (2024)
- [Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study](https://mlsanity.com/paper/2501.11214/) (2025)
- [Topology-aware Neural Flux Prediction Guided by Physics](https://mlsanity.com/paper/2506.05676/) (2025)
- [DUALFloodGNN: Physics-informed Graph Neural Network for Operational Flood Modeling](https://mlsanity.com/paper/2512.23964/) (2025)
- [Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reports](https://mlsanity.com/paper/2506.08740/) (2025)
- [MaxFloodCast: Ensemble Machine Learning Model for Predicting Peak Inundation Depth And Decoding Influencing Features](https://mlsanity.com/paper/2308.06228/) (2023)
- [Physical Scales Matter: The Role of Receptive Fields and Advection in Satellite-Based Thunderstorm Nowcasting with Convolutional Neural Networks](https://mlsanity.com/paper/2504.09994/) (2025)
- [GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interface](https://mlsanity.com/paper/2512.20813/) (2025)
- [FireGraphNet: Sub-daily wildfire spread prediction with directed graph neural networks](https://mlsanity.com/paper/2610.19191/) (2026)

## Abstract (arXiv)

The objective of this study is to develop and test a novel structured deep-learning modeling framework for urban flood nowcasting by integrating physics-based and human-sensed features. We present a new computational modeling framework including an attention-based spatial-temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting. The novelty of the computational modeling framework is threefold; first, the model is capable of considering spatial and temporal dependencies in inundation propagation thanks to the spatial and temporal graph convolutional modules; second, it enables capturing the influence of heterogeneous temporal data streams that can signal flooding status, including physics-based features such as rainfall intensity and water elevation, and human-sensed data such as flood reports and fluctuations of human activity. Third, its attention mechanism enables the model to direct its focus on the most influential features that vary dynamically. We show the application of the modeling framework in the context of Harris County, Texas, as the case study and Hurricane Harvey as the flood event. Results indicate that the model provides superior performance for the nowcasting of urban flood inundation at the census tract level, with a precision of 0.808 and a recall of 0.891, which shows the model performs better compared with some other novel models. Moreover, ASTGCN model performance improves when heterogeneous dynamic features are added into the model that solely relies on physics-based features, which demonstrates the promise of using heterogenous human-sensed data for flood nowcasting,