Fully distributed rainfall-runoff modeling using spatial-temporal graph neural network

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Authors

Zhongrun Xiang, Ibrahim Demir

Abstract

Recent studies using latest deep learning algorithms such as LSTM (Long Short-Term Memory) have shown great promise in time-series modeling. There are many studies focusing on the watershed-scale rainfall-runoff modeling or streamflow forecasting, often considering a single watershed with limited generalization capabilities. To improve the model performance, several studies explored an integrated approach by decomposing a large watershed into multiple sub-watersheds with semi-distributed structure. In this study, we propose an innovative physics-informed fully-distributed rainfall-runoff model, NRM-Graph (Neural Runoff Model-Graph), using Graph Neural Networks (GNN) to make full use of spatial information including the flow direction and geographic data. Specifically, we applied a time-series model on each grid cell for its runoff production. The output of each grid cell is then aggregated by a GNN as the final runoff at the watershed outlet. The case study shows that our GNN based model successfully represents the spatial information in predictions. NRM-Graph network has shown less over-fitting and a significant improvement on the model performance compared to the baselines with spatial information. Our research further confirms the importance of spatially distributed hydrological information in rainfall-runoff modeling using deep learning, and we encourage researchers to incorporate more domain knowledge in modeling.

DOI

https://doi.org/10.31223/X57P74

Subjects

Civil and Environmental Engineering, Earth Sciences, Engineering, Environmental Engineering, Hydrology

Keywords

Rainfall-Runoff modeling, Deep learning, fully distributed model, graph neural network, flood forecast

Dates

Published: 2022-01-15 21:48

Last Updated: 2022-01-16 05:48

License

CC BY Attribution 4.0 International

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