Leveraging Graph Neural Networks for water level prediction

Accurate water level prediction is essential for flood risk management, water resources management, inland water transportation and climate resilience. Traditional statistical methods, such as autoregressive models, and physically-based hydrological simulations, have been widely used in water level forecasting. However, these approaches often struggle to capture complex, dynamic, and nonlinear interactions in a hydrological system, particularly those affected by climate change. In recent years, machine learning models have emerged as a promising alternative, offering improved predictive accuracy and adaptability across varying environmental conditions. A special type of such models is the Graph Neural Network (GNN), which focuses especially on the reproduction of spatial dependencies, and hence it can be employed to capture the spatial dynamic of the hydrologic/hydraulic system, by treating hydrological networks as graph structures (e.g. nodes as gauges). Going one step further, GNN models can be combined with sequence-based machine learning techniques, such as the Long short-term memory (LSTM) neural network, to capture simultaneously the spatial and temporal dynamics of the system. In this work, we develop and assess a series of advanced hybrid-graph structured machine learning models (such as GNN-LSTM) to make hydrometric predictions across a long river channel. The developed models will be assessed on the basis of alternative performance metrics and against a series of traditional benchmark statistical and machine learning models such as ARIMA and LSTM respectively. As a test case, we exploit data from 19 water level gauges in the Red River of the North, which spans 885 km, serving the natural boundary between North Dakota and Minnesota and has experienced several severe historical flood events.