Search for: All records

Extreme water levels (EWLs) resulting from tropical and extratropical cyclones pose significant risks to coastal communities and their interconnected ecosystems. To date, physically-based models have enabled accurate characterization of EWLs despite their inherent high computational cost. However, the applicability of these models is limited to data-rich sites with diverse morphologic and hydrodynamic characteristics. The dependence on high quality spatiotemporal data, which is often computationally expensive, hinders the applicability of these models to regions of either limited or data-scarce conditions. To address this challenge, we present a computationally efficient deep learning framework, employing Long Short-Term Memory (LSTM) networks, to predict the evolution of EWLs beyond site-specific training stations. The framework, named LSTM-Station Approximated Models (LSTM-SAM), consists of a collection of bidirectional LSTM models enhanced with a custom attention layer mechanism embedded in the model architecture. Moreover, the LSTM-SAM framework incorporates a transfer learning approach that is applicable to target (tide-gage) stations along the U.S. Atlantic Coast. The LSTM-SAM framework demonstrates satisfactory performance with “transferable” models achieving average Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE), and Root-Mean Square Error (RMSE) ranging from 0.78 to 0.92, 0.90 to 0.97, and 0.09 to 0.18 at the target stations, respectively. Following these results, the LSTM-SAM framework can accurately predict not only EWLs but also their evolution over time, i.e., onset, peak, and dissipation, which could assist in large-scale operational flood forecasting, especially in regions with limited resources to set up high fidelity physically-based models.