Use of Neural Networks for Tsunami Maximum Height and Arrival Time Predictions

IF 6.5 3区工程技术 Q1 ENGINEERING, GEOLOGICAL

Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards Pub Date : 2022-06-13 DOI:10.3390/geohazards3020017

Juan F. Rodríguez, J. Macías, M. Castro, Marc de la Asunción, C. Sánchez-Linares

引用次数: 3

Abstract

Operational TEWS play a key role in reducing tsunami impact on populated coastal areas around the world in the event of an earthquake-generated tsunami. Traditionally, these systems in the NEAM region have relied on the implementation of decision matrices. The very short arrival times of the tsunami waves from generation to impact in this region have made it not possible to use real-time on-the-fly simulations to produce more accurate alert levels. In these cases, when time restriction is so demanding, an alternative to the use of decision matrices is the use of datasets of precomputed tsunami scenarios. In this paper we propose the use of neural networks to predict the tsunami maximum height and arrival time in the context of TEWS. Different neural networks were trained to solve these problems. Additionally, ensemble techniques were used to obtain better results.

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利用神经网络预测海啸最大高度和到达时间

在发生地震引起的海啸时，TEWS在减少海啸对世界各地人口稠密的沿海地区的影响方面发挥着关键作用。传统上，NEAM地区的这些系统依赖于决策矩阵的实现。在该地区，海啸波从产生到产生影响的时间非常短，因此不可能使用实时动态模拟来产生更准确的警报级别。在这些情况下，当时间限制如此苛刻时，使用决策矩阵的替代方案是使用预先计算的海啸情景的数据集。本文提出在TEWS环境下，利用神经网络预测海啸最大高度和到达时间。不同的神经网络被训练来解决这些问题。此外，采用集成技术获得了更好的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards Multiple-

CiteScore

8.70

自引率

10.40%

发文量

期刊介绍： Georisk covers many diversified but interlinked areas of active research and practice, such as geohazards (earthquakes, landslides, avalanches, rockfalls, tsunamis, etc.), safety of engineered systems (dams, buildings, offshore structures, lifelines, etc.), environmental risk, seismic risk, reliability-based design and code calibration, geostatistics, decision analyses, structural reliability, maintenance and life cycle performance, risk and vulnerability, hazard mapping, loss assessment (economic, social, environmental, etc.), GIS databases, remote sensing, and many other related disciplines. The underlying theme is that uncertainties associated with geomaterials (soils, rocks), geologic processes, and possible subsequent treatments, are usually large and complex and these uncertainties play an indispensable role in the risk assessment and management of engineered and natural systems. Significant theoretical and practical challenges remain on quantifying these uncertainties and developing defensible risk management methodologies that are acceptable to decision makers and stakeholders. Many opportunities to leverage on the rapid advancement in Bayesian analysis, machine learning, artificial intelligence, and other data-driven methods also exist, which can greatly enhance our decision-making abilities. The basic goal of this international peer-reviewed journal is to provide a multi-disciplinary scientific forum for cross fertilization of ideas between interested parties working on various aspects of georisk to advance the state-of-the-art and the state-of-the-practice.