With the increasing impact of climate change and relative sea level rise, low-lying coastal communities face growing risks from recurrent nuisance flooding and storm tides. Thus, timely and reliable predictions of coastal water levels are critical to resilience in vulnerable coastal areas. Over the past decade, there has been increasing interest in utilizing machine learning (ML) based models for emulation and prediction of coastal water levels. However, flood advisory systems still rely on running computationally demanding hydrodynamic models. To alleviate the computational burden, these physics-based models are either run at small scales with high resolution or at large scales with low resolution. While ML-based models are very fast, they face challenges in terms of ensuring reliability and ability to capture any surge levels. In this paper, we develop a deep neural network for spatiotemporal prediction of water levels in coastal areas of the Chesapeake Bay in the U.S. Our model relies on data from numerical weather prediction models as the atmospheric input and astronomical tide levels, while its outputs are time series of predicted water levels at several tide gauge locations across the Chesapeake Bay. We utilized a CNN-LSTM setting as the architecture of the model. The CNN part extracts the features from a sequence of gridded wind fields and fuses its output to several independent LSTM units. The LSTM units concatenate the atmospheric features with respective astronomical tide levels and produce water level time series. The novel contribution of the present work is in spatiotemporality and in prioritization of the physical relationships in the model to maintain a high analogy to hydrodynamic modeling, either in the network architecture or in the selection of predictors and predictands. The results show that this setting yields a strong performance in predicting coastal water levels that cause flooding from minor to major levels. We also show that the model stands up successfully to the rigorous comparison with a high-fidelity ADCIRC model, yielding mean RMSE and correlation coefficient of 14.3 cm and 0.94, respectively, in two extreme cases, versus 12.30 cm and 0.96 for the ADCIRC model. The results highlight the practical feasibility of employing fast yet inexpensive data-driven models for resilient coastal management.

中文翻译：

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沿海水位快速时空预测的深度学习模型

随着气候变化和相对海平面上升的影响日益加大，低洼沿海社区面临着反复出现的令人讨厌的洪水和风暴潮的越来越大的风险。因此，及时、可靠地预测沿海水位对于脆弱沿海地区的恢复能力至关重要。在过去的十年中，人们对利用基于机器学习 (ML) 的模型来模拟和预测沿海水位越来越感兴趣。然而，洪水咨询系统仍然依赖于运行计算要求较高的水动力模型。为了减轻计算负担，这些基于物理的模型要么在高分辨率的小尺度下运行，要么在低分辨率的大尺度下运行。虽然基于机器学习的模型速度非常快，但它们在确保可靠性和捕获任何浪涌水平的能力方面面临挑战。在本文中，我们开发了一种深度神经网络，用于美国切萨皮克湾沿海地区水位的时空预测。我们的模型依赖于数值天气预报模型的数据作为大气输入和天文潮位，而其输出是时间切萨皮克湾多个潮汐测量点的一系列预测水位。我们使用 CNN-LSTM 设置作为模型的架构。 CNN 部分从一系列网格化风场中提取特征，并将其输出融合到多个独立的 LSTM 单元。 LSTM 单元将大气特征与相应的天文潮位连接起来，生成水位时间序列。本工作的新颖贡献在于时空性和模型中物理关系的优先级，以在网络架构或预测变量和预测对象的选择中保持与流体动力学建模的高度相似性。结果表明，该设置在预测导致小洪水到大洪水的沿海水位方面表现出色。我们还表明，该模型成功地经受住了与高保真 ADCIRC 模型的严格比较，在两种极端情况下，平均 RMSE 和相关系数分别为 14.3 cm 和 0.94，而 ADCIRC 模型的平均 RMSE 和相关系数分别为 12.30 cm 和 0.96。结果凸显了采用快速且廉价的数据驱动模型进行弹性海岸管理的实际可行性。