Enhancing Flood Forecast: A Deep Learning Approach Combining NWP, Hydrology and Hydrodynamics

Coastal cities located in estuaries often face significant risks from both riverine and tidal flooding due to their low-lying locations. Accurately predicting flood water levels in a complex urban environment is challenging because multiple factors interact – upstream river flow, heavy rainfall, tides and storm surges all play a role. This research explores a new approach to compound coastal-fluvial flood forecasting using deep learning: a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The goal is to forecast water levels in an estuary as well as flood water levels over an urban floodplain with a lead time of 1 to 33 hours. The model is specifically designed to effectively combine various hydrological, coastal and meteorological data sources.

Coastal city of Cork, the second-largest city in Ireland and one that is frequently affected by compound coastal-fluvial flooding is used as a case study. In this research, we use high-resolution precipitation forecasts from the NWP model operated by Met Eireann, river flow data from the River Lee catchment, and tidal/surge information from the MSN_Flood hydrodynamic model of the Cork Harbour.

Our proposed CNN-LSTM architecture combines the strengths of these deep learning methods. The CNN component efficiently identifies important spatial patterns from the rainfall forecasts and model outputs that suggest potential flooding. The LSTM component then captures how water levels change over time, enabling the model to learn the evolution of flood conditions.

Historical flood events in Cork City form the basis for training our deep learning model. This historical data, combined with real-time data streams from NWP, river gauge records and hydrodynamic model, allows the CNN-LSTM network to learn the intricate relationships between upstream riverine conditions and downstream sea water levels. This system has the potential to significantly improve flood preparedness and response in Cork City, enabling earlier warnings and proactive measures to protect communities from flood damage. Additional data, such as soil moisture and land cover information are also used to enhance the model’s accuracy and robustness.