Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks

2021/07/07 by Muhammed Sit, Sit, Muhammed, Bekir Demiray +5 · 1 citation
Computer Science · Engineering · Environmental Science · #FOS: Computer and information sciences #FOS: Electrical engineering #Flood Risk Assessment and Management #Hydrological Forecasting Using AI #Hydrology and Watershed Management Studies #Machine Learning (cs.LG) #Signal Processing (eess.SP) #cs.LG #eess.SP #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2107.07039

4 pages, Accepted to Tackling Climate Change with Machine Learning workshop at ICML 2021

arxiv created 2021/07/07 · openalex publication_date 2021/07/07 · arxiv updated 2021/07/16 · openalex created_date 2021/07/19 · openalex updated_date 2026/07/28

Abstract

The frequency and impact of floods are expected to increase due to climate change. It is crucial to predict streamflow, consequently flooding, in order to prepare and mitigate its consequences in terms of property damage and fatalities. This paper presents a Graph Convolutional GRUs based model to predict the next 36 hours of streamflow for a sensor location using the upstream river network. As shown in experiment results, the model presented in this study provides better performance than the persistence baseline and a Convolutional Bidirectional GRU network for the selected study area in short-term streamflow prediction.

Citations

• Long Short-Term Memory
• Fast Graph Representation Learning with PyTorch Geometric
• Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
• Climate change impact on flood and extreme precipitation increases with water availability
• Graph neural networks: A review of methods and applications
• A Comprehensive Survey on Graph Neural Networks

Cited by

• HydroGAT: Distributed Heterogeneous Graph Attention Transformer for Spatiotemporal Flood Prediction

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