Departamento de Obras Civiles, Universidad Técnica Federico Santa María, Valparaíso, 2390123, Chile.
Departamento de Ciencia de Datos e Informática, Universidad de Playa Ancha Valparaíso, Valparaiso, Chile.
Sci Rep. 2022 Jun 20;12(1):10321. doi: 10.1038/s41598-022-13788-9.
Tsunamis are natural phenomena that, although occasional, can have large impacts on coastal environments and settlements, especially in terms of loss of life. An accurate, detailed and timely assessment of the hazard is essential as input for mitigation strategies both in the long term and during emergencies. This goal is compounded by the high computational cost of simulating an adequate number of scenarios to make robust assessments. To reduce this handicap, alternative methods could be used. Here, an enhanced method for estimating tsunami time series using a one-dimensional convolutional neural network model (1D CNN) is considered. While the use of deep learning for this problem is not new, most of existing research has focused on assessing the capability of a network to reproduce inundation metrics extrema. However, for the context of Tsunami Early Warning, it is equally relevant to assess whether the networks can accurately predict whether inundation would occur or not, and its time series if it does. Hence, a set of 6776 scenarios with magnitudes in the range [Formula: see text] 8.0-9.2 were used to design several 1D CNN models at two bays that have different hydrodynamic behavior, that would use as input inexpensive low-resolution numerical modeling of tsunami propagation to predict inundation time series at pinpoint locations. In addition, different configuration parameters were also analyzed to outline a methodology for model testing and design, that could be applied elsewhere. The results show that the network models are capable of reproducing inundation time series well, either for small or large flow depths, but also when no inundation was forecast, with minimal instances of false alarms or missed alarms. To further assess the performance, the model was tested with two past tsunamis and compared with actual inundation metrics. The results obtained are promising, and the proposed model could become a reliable alternative for the calculation of tsunami intensity measures in a faster than real time manner. This could complement existing early warning system, by means of an approximate and fast procedure that could allow simulating a larger number of scenarios within the always restricting time frame of tsunami emergencies.
海啸是一种自然现象，虽然偶尔发生，但会对沿海环境和定居点造成重大影响，尤其是在生命损失方面。准确、详细和及时地评估灾害情况对于长期和紧急情况下的缓解策略都是至关重要的。由于需要模拟足够数量的场景来进行稳健评估，因此这一目标的计算成本很高。为了降低这一障碍，可以使用替代方法。在这里，考虑使用一维卷积神经网络模型 (1D CNN) 来增强海啸时间序列估计的方法。虽然深度学习在这个问题上的应用并不新鲜，但大多数现有研究都集中在评估网络再现淹没指标极值的能力上。然而，对于海啸预警的情况，同样重要的是评估网络是否能够准确预测是否会发生淹没以及如果发生淹没，其时间序列。因此，使用了一组 [公式：见文本] 8.0-9.2 震级范围内的 6776 个场景来设计两个具有不同水动力行为的海湾的几个 1D CNN 模型，这些模型将使用廉价的海啸传播数值模拟的低分辨率输入来预测准确位置的淹没时间序列。此外，还分析了不同的配置参数，以概述一种适用于模型测试和设计的方法，该方法可以在其他地方应用。结果表明，网络模型能够很好地再现淹没时间序列，无论是小流量深度还是大流量深度，甚至在没有预测到淹没的情况下，虚假警报或漏报的情况也很少。为了进一步评估性能，使用两个过去的海啸对模型进行了测试，并与实际淹没指标进行了比较。得到的结果很有希望，所提出的模型可以成为一种可靠的替代方法，用于以快于实时的方式计算海啸强度度量。这可以通过一种近似和快速的程序来补充现有的预警系统，该程序可以在海啸紧急情况的始终受限的时间框架内模拟更多数量的场景。
