Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates (2310.07631v1)

Abstract: Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of extreme weather events, resulting in elevated and frequent flood risk. Therefore, accurate and timely flood forecasting in coastal river systems is critical to facilitate good flood management. However, the computational tools currently used are either slow or inaccurate. In this paper, we propose a Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems. More specifically, FloodGTN learns the spatio-temporal dependencies of water levels at different monitoring stations using Graph Neural Networks (GNNs) and an LSTM. It is currently implemented to consider external covariates such as rainfall, tide, and the settings of hydraulic structures (e.g., outflows of dams, gates, pumps, etc.) along the river. We use a Transformer to learn the attention given to external covariates in computing water levels. We apply the FloodGTN tool to data from the South Florida Water Management District, which manages a coastal area prone to frequent storms and hurricanes. Experimental results show that FloodGTN outperforms the physics-based model (HEC-RAS) by achieving higher accuracy with 70% improvement while speeding up run times by at least 500x.

• The paper introduces FloodGTN, a novel model that leverages graph networks, transformers, and LSTM to achieve 70% better flood forecasting accuracy.
• It employs both parallel and series configurations to integrate external covariates with spatio-temporal dynamics effectively.
• Experimental results on South Florida’s coastal systems show superior computational efficiency and accuracy compared to physics‐based models.

Enhanced Flood Forecasting Through Graph Transformer Networks: A Study on South Florida's Coastal Systems

Introduction to FloodGTN

In recent years, the urgency to enhance flood forecasting mechanisms has become more evident due to the escalating impacts of climate change, which have led to a heightened frequency and intensity of flood events. Traditional methods leveraging physics-based models, while detailed, have been challenged by their computational intensity and inability to quickly adapt to real-time data influx. In an innovative approach addressing these limitations, a new paper introduces the Flood prediction tool using Graph Transformer Network (FloodGTN), employing Graph Neural Networks (GNNs) and Transformer architectures. This tool aims to accurately predict flood levels by comprehensively analyzing spatio-temporal dependencies and external covariates, revolutionizing flood management strategies.

Methodological Insights

FloodGTN capitalizes on a deep learning framework that integrates the strengths of various architectures to predict water levels in river systems. The model is distinctive in its use of GNNs, Transformers, and LSTM networks, facilitating a nuanced understanding of both the spatial layout of river systems and the temporal progression of water levels.

• FloodGTN-Parallel and FloodGTN-Series: The paper delineates two versions of FloodGTN, namely the parallel and series configurations. The former simultaneously processes feature representations from external covariates and spatiotemporal dynamics using a Transformer and a GCN-LSTM, respectively. Contrarily, the series version sequentially processes these components, offering a different perspective on data handling.
• External Covariates: Recognizing the reliability of modern predictions for certain covariates (e.g., rainfall, tide levels), FloodGTN incorporates these data points into its forecasting model. This approach acknowledges the importance of external factors in accurately predicting flood levels, a step forward in making flood forecasting models more comprehensive.

Experimental Validation

The paper's experimentation involved applying FloodGTN to a dataset from the South Florida Water Management District, emphasizing regions prone to frequent flooding. The results underscored the superiority of FloodGTN over traditional physics-based models like HEC-RAS, showing a 70% improvement in accuracy and a significant enhancement in computational efficiency.

• Impact of Future Covariates: The analysis confirmed that incorporating predictions of future external covariates significantly bolsters the model's predictive accuracy, an affirmation of FloodGTN's design philosophy.
• Comparative Analysis: Benchmarking FloodGTN against various deep learning and physics-based models revealed its superior performance, not only in terms of prediction accuracy but also in computational speed post-training, indicating its potential as a real-time flood forecasting tool.

Concluding Remarks

The introduction of FloodGTN marks a significant leap towards more accurate, efficient, and comprehensive flood forecasting mechanisms. By synergizing graph neural networks, transformers, and profound insights into river system dynamics, this tool transcends traditional limitations, paving the way for resilient flood management systems. Future explorations could extend its application across diverse hydrological models and explore the integration of even more granular covariates to further refine its predictive capabilities.

Implications and Future Directions

The theoretical and practical implications of FloodGTN extend beyond immediate flood forecasting improvements. The tool's ability to rapidly process and predict based on large, complex datasets opens avenues for real-time disaster management and risk reduction strategies. Furthermore, the success of FloodGTN encourages a reevaluation of existing models and raises the potential for the integration of AI and machine learning into broader environmental and emergency management frameworks. Looking forward, continuous advancements in predictive accuracy and computational efficiency remain pivotal. Additionally, extending the model's application to cover a wider range of environmental phenomena could significantly impact global sustainability and disaster resilience efforts.