Storm surge is a major natural hazard for coastal regions, responsible both for significant property damage and loss of life. Accurate, efficient models of storm surge are needed both to assess long-term risk and to guide emergency management decisions. While high-fidelity ocean circulation models such as the ADvanced CIRCulation (ADCIRC) model can accurately predict storm surge, they are very computationally expensive. Consequently, there have been a number of efforts in recent years to develop data-driven surrogate models for storm surge. While these models can attain good accuracy and are highly efficient, they are often limited to a small geographical region and a fixed set of output locations. We develop a novel surrogate model for peak storm surge prediction based on gradient boosting. Unlike most surrogate approaches, our model is not explicitly constrained to a fixed set of output locations or specific geographical region. The model is trained with a database of 446 synthetic storms that make landfall on the Texas coast and obtains a mean absolute error of 0.25 meters. We additionally present a test of the model on Hurricanes Ike (2008) and Harvey (2017).
翻译:风暴潮是沿海地区的主要自然危害,造成重大财产损失和生命损失,需要准确、高效的风暴潮模型来评估长期风险并指导应急管理决定。虽然高迷幻性海洋环流模型,如ADCIRCulate(ADCIRC)模型可以准确预测风暴潮,但它们在计算上非常昂贵。因此,近年来为开发由数据驱动的风暴潮代谢模型做出了许多努力。这些模型可以达到良好的准确性,并且效率很高,但它们往往局限于一个小地理区域和一套固定的产出地点。我们开发了一个新的基于梯度加速的高峰风暴潮预测替代模型。与大多数替代方法不同,我们的模型并不明确局限于一套固定的产出地点或特定地理区域。模型经过446个合成风暴数据库的培训,这些风暴在得克萨斯海岸登陆,并获得0.25米的绝对平均误差。我们还介绍了关于艾克飓风(2008年)和哈维(2017年)的模型测试。