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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. 

Abstract Extreme water levels (EWLs) resulting from cyclones pose significant flood hazards and risks to coastal communities and interconnected ecosystems. To date, physically based models have enabled accurate prediction of EWLs despite their inherent high computational cost. However, the applicability of these models is limited to data‐rich sites with diverse 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 Long Short‐Term Memory (LSTM) network framework 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 mechanism layer embedded in the architecture. LSTM‐SAM incorporates a transfer learning approach applicable to target (tide‐gage) stations along the U.S. Atlantic Coast. Importantly, LSTM‐SAM helps analyze: (a) the underlying limitations associated with transfer learning, (b) evaluate EWL predictions beyond training domains, and (c) capture the evolution of EWL caused by tropical and extratropical cyclones. The framework demonstrates satisfactory performance with “transferable” models achieving 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 0.09–0.18 m at the target stations, respectively. We show that LSTM‐SAM can accurately predict not only EWLs but also their evolution over time, that is, onset, peak, and dissipation, which could assist in operational flood forecasting in regions with limited resources to set up physically based models. 

Abstract Estimating uncertainty in flood model predictions is important for many applications, including risk assessment and flood forecasting. We focus on uncertainty in physics‐based urban flooding models. We consider the effects of the model's complexity and uncertainty in key input parameters. The effect of rainfall intensity on the uncertainty in water depth predictions is also studied. As a test study, we choose the Interconnected Channel and Pond Routing (ICPR) model of a part of the city of Minneapolis. The uncertainty in the ICPR model's predictions of the floodwater depth is quantified in terms of the ensemble variance using the multilevel Monte Carlo (MC) simulation method. Our results show that uncertainties in the studied domain are highly localized. Model simplifications, such as disregarding the groundwater flow, lead to overly confident predictions, that is, predictions that are both less accurate and uncertain than those of the more complex model. We find that for the same number of uncertain parameters, increasing the model resolution reduces uncertainty in the model predictions (and increases the MC method's computational cost). We employ the multilevel MC method to reduce the cost of estimating uncertainty in a high‐resolution ICPR model. Finally, we use the ensemble estimates of the mean and covariance of the flood depth for real‐time flood depth forecasting using the physics‐informed Gaussian process regression method. We show that even with few measurements, the proposed framework results in a more accurate forecast than that provided by the mean prediction of the ICPR model.