Rapid prediction of peak storm surge from tropical cyclone track time series using machine learning
Rapid prediction of peak storm surge from tropical cyclone track time series using machine learning
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DOI：
10.31223/x5ns6h
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发表时间：
2021-07
影响因子：
4.4
通讯作者：
Jun‐Whan Lee;J. Irish;M. Bensi;Douglas C. Marcy
中科院分区：
文献类型：
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作者：
Jun‐Whan Lee;J. Irish;M. Bensi;Douglas C. Marcy
对于为设计保护沿海社区生命和财产的系统所用的评估提供信息，必须快速和准确地预测整个沿海地区的风暴潮峰值。近年来，在高保真、基于物理的数值模型方面取得了重大进展，但使用这些模型进行概率预测和概率风险评估需要大量的计算。最近提出了几种基于现有高保真合成风暴潮模拟数据库的代理模拟方法，以减少计算负担而不降低精度。然而，在以前的研究中，替代模型方法依赖于某个时刻(通常是在登陆或接近登陆时)的热带气旋条件，而热带气旋条件并不总是与风暴潮峰值最相关。本文提出了一种结合主成分分析和k-均值聚类的一维卷积神经网络模型(C1PKNet)，该模型可以根据热带气旋条件的时间序列，即风暴路径，快速预报沿海地区的风暴潮峰值。C1PKNet模型针对美国切萨皮克湾地区进行了训练和交叉验证，使用了现有的1031个高保真风暴潮模拟数据库，包括登陆和绕过风暴。此外，根据对三次历史飓风(2003年伊莎贝尔飓风、2011年艾琳飓风和2012年桑迪飓风)的观测，对C1PKNet模型的性能进行了评估。结果表明，C1PKNet模式计算效率高，能从实际热带气旋路径时间序列中预测风暴潮峰值。我们相信，这一新的替代模型可以通过提供快速风暴潮预测来增强沿海地区的复原力。
Rapid and accurate prediction of peak storm surges across an extensive coastal region is necessary to inform assessments used to design the systems that protect coastal communities’ life and property. Significant advances in high-fidelity, physics-based numerical models have been made in recent years, but use of these models for probabilistic forecasting and probabilistic hazard assessment is computationally intensive. Several surrogate modeling approaches based on existing databases of high-fidelity synthetic storm surge simulations have been recently suggested to reduce computational burden without substantial loss of accuracy. In these previous studies, however, the surrogate modeling approaches relied on a tropical cyclone condition at one moment (usually at or near landfall), which is not always most correlated with the peak storm surge. In this study, a new one-dimensional convolutional neural network model combined with principal component analysis and a k-means clustering (C1PKNet) is presented that can rapidly predict peak storm surge across an extensive coastal region from time-series of tropical cyclone conditions, namely the storm track. The C1PKNet model was trained and cross-validated for the Chesapeake Bay area of the United States using existing database of 1031 high-fidelity storm surge simulations, including both landfalling and bypassing storms. Moreover, the performance of the C1PKNet model was evaluated based on observations from three historical hurricanes (Hurricane Isabel in 2003, Hurricane Irene in 2011, and Hurricane Sandy in 2012). The results indicate that the C1PKNet model is computationally e cient and can predict peak storm surges from realistic tropical cyclone track time-series. We believe that this new surrogate model can enhance coastal resilience by providing rapid storm surge predictions.