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Estimation of global coastal sea level extremes using neural networks
Environmental Research Letters ( IF 6.7 ) Pub Date : 2020-07-05 , DOI: 10.1088/1748-9326/ab89d6
Nicolas Bruneau 1, 2 , Jeff Polton 1 , Joanne Williams 1 , Jason Holt 1
Affiliation  

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 m...

中文翻译:

使用神经网络估算全球沿海海平面极限

准确预测包括潮汐和风暴潮在内的总海平面,对于保护和管理我们的沿海环境至关重要。然而,动态预测海平面极限在计算上是昂贵的。在这里,一种基于人工神经网络集成的新颖替代方案被用来在全球各地的600多个潮汐仪上进行独立训练,从而根据潮汐谐波和每个地点的大气状况来预测总海平面。结果表明,神经网络(NN)具有全球一致的高技能,可以捕获全球范围内海平面的海面变化。尽管可以通过多元线性回归来捕捉主要的大气动力,但只有NNs才能预测复杂沿海环境中的大气动力强度,潮汐波动和潮汐非线性。此外,非线性NN方法提供了一个简单且一致的框架,可通过概率预测来评估不确定性。这些新的和廉价的...
更新日期:2020-07-06
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