Numerical experiments on tsunami flow depth prediction for clustered areas using regression and machine learning models

Emergency responses during a massive tsunami disaster require information on the flow depth of land for rescue operations. This study aims to predict tsunami flow depth distribution in real time using regression and machine learning. Training data of 3480 earthquake-induced tsunamis in the Nankai Trough were constructed by numerical simulations. Initially, the k-means method was used to discriminate the areas with approximately the same flow depth. The number of clustered areas was 18, and the standard deviation of the flow depth data in a cluster was 0.46 m on average. The objective variables were the mean and standard deviation of the flow depth in the clustered areas. The explanatory variables were the maximum deviation of the water pressure at the seafloor observation points of the DONET observatory. We generated multiple regression equations for a power law using these datasets and the conjugate gradient method. Further, we employed the multilayer perceptron method, a machine learning technique, to evaluate the prediction performance. Both methods accurately predicted the tsunami flow depth calculated by testing 11 earthquake scenarios in the cabinet office of the government of Japan. The RMSE between the predicted and the true (via forward tsunami calculations) values of the mean flow depth ranged from 0.34–1.08 m. In addition to large-scale tsunami prediction systems, prediction methods with a robust and light computational load as used in this study are essential to prepare for unforeseen situations during large-scale earthquakes and tsunami disasters

High-resolution, Quantitative Tsunami simulation on New Earth Simulator

2015年より本格稼働した新地球シミュレータ（SX-ACE）で南海トラフ地震を想定した大規模津波シミュレーションを実施し，多数のシナリオに基づく和歌山県沿岸域における津波浸水データベースを構築した．津波シミュレーションを効率的に実施するために，津波シミュレーションコード（JAGURS）の最適化やスケーラビリティの評価，マルチジョブコントロールを実施した．津波シミュレーションの効率化により，3万ケースを超える津波シミュレーションを3か月という短期間で完了させた．津波浸水データベースは和歌山県が運用している津波浸水予測システムに組み込まれ，気象庁以外で初めて和歌山県が独自に津波予報業務をはじめている．2016年ハイパフォーマンスコンピューティングと計算科学シンポジウム（2016年6月6日～7日, 東北大学片平キャンパス

A nonlinear parametric model based on a power law relationship for predicting the coastal tsunami height

When a subduction-zone earthquake occurs, the tsunami height must be predicted to cope with the damage generated by the tsunami. Therefore, tsunami height prediction methods have been studied using simulation data acquired by large-scale calculations. In this research, we consider the existence of a nonlinear power law relationship between the water pressure gauge data observed by the Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) and the coastal tsunami height. Using this relationship, we propose a nonlinear parametric model and conduct a prediction experiment to compare the accuracy of the proposed method with those of previous methods and implement particular improvements to the extrapolation accuracy

Probabilistic Landslide-Generated Tsunamis in the Indus Canyon, NW Indian Ocean, Using Statistical Emulation

The Indus Canyon in the northwestern Indian Ocean has been reported to be the site of numerous submarine mass failures in the past. This study is the first to investigate potential tsunami hazards associated with such mass failures in this region. We employed statistical emulation, i.e. surrogate modelling, to efficiently quantify uncertainties associated with slump-generated tsunamis at the slopes of the canyon. We simulated 60 slump scenarios with thickness of 100–300 m, width of 6–10.5 km, travel distances of 500–2000 m and submergence depth of 250–450 m. These scenarios were then used to train the emulator and predict 500,000 trial scenarios in order to study probabilistically the tsunami hazard over the near field. Due to narrow–deep canyon walls and the shallow continental shelf in the adjacent regions ( <100 m water depth), the tsunami propagation has a unique pattern as an ellipse stretched in the NE–SW direction. The results show that the most likely tsunami amplitudes and velocities are approximately 0.2–1.0 m and 2.5–13 m/s, respectively, which can potentially impact vessels and maritime facilities. We demonstrate that the emulator-based approach is an important tool for probabilistic hazard analysis since it can generate thousands of tsunami scenarios in few seconds, compared to days of computations on High Performance Computing facilities for a single run of the dispersive tsunami solver that we use here

Social Implementation of Tsunami Prediction System on Wakayama by Using DONET Information

High possibility of occurrence of earthquake with M9 or lager in the Nankai subduction zone was pointed out by Cabinet Office of Japanese government. Local governments along the area revised estimation of tsunami damages and attempt to reconstruct action plans for the disaster prevention. However, according to Cabinet office of Japanese government, the coastal area near the rupture zone receives huge tsunami within a few minutes after the earthquake happens. To take actions against the severe situation, we need a high-speed, and high-accurate tsunami prediction system. Baba et al. (2014) investigated the possibility for use of a concept of tsunami amplification in the early tsunami prediction. They computed tsunami waveforms at the twenty pressure gauges of dense ocean floor network system for earthquakes and tsunamis (DONET) and at prediction points near shore. They found clear correlations between them because tsunami height basically depends on the topography (bathymetry) during its propagation. In this study, an early tsunami prediction system using the concept of tsunami amplification was societally implemented in 6 regional areas in Wakayama Prefecture. We constructed a tsunami database that contains pre-computed tsunamis offshore and nearshore from 1506 earthquake scenarios may occur in the Nankai subduction zones. The new system detects first arrivals of earthquake and tsunami from DONET data in real time, and calculate average value of absolute observed pressure values among twenty DONET stations. The value is used to select an appropriate scenario from the tsunami database. Prediction accuracy of the system was also investigated by using cases of the 1944 Tonankai earthquake and the scenario earthquake provided by the Cabinet office. As a result, we found that predicted inundation area to be overestimated as the safety of the prediction.南海トラフの沈み込み帯において，M9クラス巨大地震とそれにともなう巨大津波の発生の可能性が内閣府により指摘されて久しい。この津波被害想定によると，地震域近傍の沿岸地域では地震発生から数分後に巨大な津波が到達してしまうため，津波防災に向けた行動計画の再構築や人的被害軽減のための迅速な対応策の検討が極めて重要になる。その対応策のひとつとして，高速かつ高精度な即時津波予測が有効と考えられる。本研究では，地震と津波観測に向けた稠密海底観測網(DONET)による沖合観測網を利用した即時津波予測システムを構築し，和歌山県沿岸6地域において実装を行い，その有効性の検討を行った。本システムにより，地震と津波の初動到達時間を即時評価できること，沿岸津波高や浸水域の即時予測が可能であることを示した。さらに，1944年昭和東南海地震の事例と内閣府のM9クラス巨大地震の波源シナリオを用いて本システムの予測精度を検証した。本システムで即時予測される沿岸津波高や浸水域面積はやや過大評価傾向にあるものの，おおむね安全側の予測結果となり，津波防災上有効なシステムであることを示した

Variation analysis of multiple tsunami inundation models

Researchers have developed tsunami inundation models based on nonlinear shallow water equations to estimate tsunami propagation and inundation. However, their empirical results are not in perfect agreement with those of other research institutes, even though the same governing equations are used. Therefore, we quantitatively evaluated the variability of tsunami simulations in this study. Several research institutes have conducted tsunami simulations under the same input conditions using tsunami inundation models adopted for tsunami hazard assessment, resulting in a certain degree of variability among them. By examining the spatial and temporal differences in various physical quantities, we identified the characteristic topography where the variability between tsunami simulations increases. A novel method for calculating statistics from the area integrals of physical quantities was proposed to demonstrate the variability in the overall simulation results. In addition, the effects of different setting parameters and computational environments on the simulation results of a single model were evaluated. The findings of this study are expected to not only serve as a basis to verify the reliability of source codes employed by users of the tsunami inundation model, but also contribute useful technical information to advance probabilistic tsunami hazard assessment in the future

Synthesizing sea surface height change including seismic waves and tsunami using a dynamic rupture scenario of anticipated Nankai trough earthquakes

The development of offshore observation technology will provide researchers with tsunami records from within an earthquake focal area, but this will create new problems. Because seismic waves coexist with tsunami inside a focal area, the seismic waves could act as noise for the tsunami signal. This study shows an efficient method to calculate sea surface height change caused by an earthquake including both seismic waves and tsunami. Simulation results indicate that seismic waves overlap with tsunami; both affect the change in sea surface height although most previous tsunami studies have neglected the contribution of seismic waves. We also numerically simulated the sea-surface displacement wavefield and hypothesized results for an anticipated rupture scenario of a huge earthquake that may possibly occur in the Nankai Trough, Japan. The synthesized record could be used to evaluate the performance of a real-time tsunami prediction method. Additionally, we discussed the similarity and difference between two kinds of tsunami waveforms: the displacement of the sea surface and the pressure change at the sea bottom. Although seismic waves appeared in both waveforms, the contribution of seismic waves was lower in the displacement at the sea surface than in the pressure change at the sea bottom

Impact of future tsunamis from the Java trench on household welfare: Merging geophysics and economics through catastrophe modelling

This paper presents the first end-to-end example of a risk model for loss of assets in households due to possible future tsunamis. There is a significant need for Government to assess the generic risk to buildings, and the concrete impact on the full range of assets of households, including the ones that are key to livelihoods such as agricultural land, fishing boats, livestock and equipment. Our approach relies on the Oasis Loss Modelling Framework to integrate hazard and risk. We first generate 25 representative events of tsunamigenic earthquakes off the Southern coast of Java, Indonesia. We then create a new vulnerability function based upon the Indonesian household survey STAR1 of how much assets have been reduced in each household after the 2004 tsunami. We run a multinomial logit regression to precisely allocate the probabilistic impacts to bins that correspond with levels of financial reduction in assets. We focus on the town of Cilacap for which we build loss exceedance curves, which represent the financial losses that may be exceeded at a range of future timelines, using future tsunami inundations over a surveyed layout and value of assets over the city. Our loss calculations show that losses increase sharply, especially for events with return periods beyond 250 years. These series of computations will allow more accurate investigations of impacts on livelihoods and thus will help design mitigation strategies as well as policies to minimize suffering from tsunamis.Lloyd's Tercentenary Research Foundation; Lighthill Risk Network; Alan Turing Institute project "Uncertainty Quantification of multi-scale and multiphysics computer models: applications to hazard and climate models", EPSRC EP/N510129/1; Royal Society, the United Kingdom CHL/R1/180173