Estimation of global coastal sea level extremes using neural networks
Estimation of global coastal sea level extremes using neural networks
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DOI：
10.1088/1748-9326/ab89d6
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发表时间：
2020-07-01
影响因子：
6.7
通讯作者：
Holt, Jason
中科院分区：
文献类型：
--
作者：
Bruneau, Nicolas;Polton, Jeff;Holt, Jason
准确预测包括潮汐和风暴潮在内的总海平面是保护和管理我们沿海环境的关键。然而，动态地预测海平面极端是计算昂贵的。在这里，一种新的替代方案的基础上，在世界各地的600多个验潮站独立训练的人工神经网络的合奏，用于预测总海平面的基础上，潮汐谐波和大气条件在每个站点。结果表明，全球一致的高技能的神经网络（NN），以捕捉海洋的变化在地球仪。虽然主要的大气驱动的动态可以捕获与多元线性回归，大气驱动的加剧，潮汐浪涌和潮汐潮汐非线性在复杂的沿海环境中，只能预测与NN。此外，非线性神经网络方法提供了一个简单而一致的框架，通过概率预测来评估不确定性。这些新的和廉价的方法是相对容易设置，并可能是一个有价值的工具，结合更昂贵的动态模型，以提高当地的弹性。
Accurately predicting total sea-level including tides and storm surges is key to protecting and managing our coastal environment. However, dynamically forecasting sea level extremes is computationally expensive. Here a novel alternative based on ensembles of artificial neural networks independently trained at over 600 tide gauges around the world, is used to predict the total sea-level based on tidal harmonics and atmospheric conditions at each site. The results show globally-consistent high skill of the neural networks (NNs) to capture the sea variability at gauges around the globe. While the main atmosphere-driven dynamics can be captured with multivariate linear regressions, atmospheric-driven intensification, tide-surge and tide-tide non-linearities in complex coastal environments are only predicted with the NNs. In addition, the non-linear NN approach provides a simple and consistent framework to assess the uncertainty through a probabilistic forecast. These new and cheap methods are relatively easy to setup and could be a valuable tool combined with more expensive dynamical model in order to improve local resilience.