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In this study, we present an optimization method for determining a cost‐effective sparse configuration for tsunami gauges to realize the reconstruction of high‐resolution wave height distribution throughout the target region based on the concept of super‐resolution. This optimization method consists of three procedures. First, we generate time series data of tsunami wave heights at synthetic gauges by conducting numerical simulations of various earthquake and tsunami scenarios at the target site. Next, we apply proper orthogonal decomposition to the synthetic tsunami data to extract the spatial features of the wave height distribution. Finally, according to these spatial features, an optimization process is performed to determine a sparse configuration of synthetic gauges. In the optimization, the optimal gauges are sequentially selected from the set of synthetic gauges to reconstruct the wave height distribution with the highest accuracy. Targeting hypothetical Nankai Trough earthquakes and tsunamis, we determine the optimal configuration near Shikoku and demonstrate the wave height reconstruction capability of the approach by comparing the performance of networks with optimally and randomly placed gauges. The results indicate that coastal gauges contribute more to improving the reconstruction accuracy and that a configuration with 21 optimal gauges has satisfactory performance. In addition, we assess the performance of the existing NOWPHAS network installed in the Shikoku region and find that the reconstruction performance of the existing network is equivalent to that of the optimal gauge network. Plain Language Summary: This study introduces a method of optimizing the sparse locations where actual tsunami gauges should be installed to obtain information on tsunami wave heights at any given point. By optimizing the locations of the observation points, it is possible to extend observations recorded at only a small number of points to obtain a good approximation to data at other points where the tsunami was not directly observed. First, numerical simulations are performed based on assumed earthquake and tsunami scenarios to generate synthetic time series data of tsunami waves. Then, by applying proper orthogonal decomposition to the obtained synthetic data, the characteristics of the tsunami wave height distribution are extracted. Finally, these characteristics are used to perform optimization by sequentially selecting the best gauges from among a set of candidate points to reconstruct wave height information for the entire target area, thereby determining the placements of a limited number of gauges. In a numerical demonstration example simulating a Nankai Trough earthquake and tsunami, the placement of gauges off the coast of Shikoku is optimized, and it is shown that the wave heights at arbitrary points over the entire area can be reproduced using data from at least 21 optimally placed gauges. Key Points: An optimization method implementing a greedy algorithm is presented to design a cost‐effective observational network of tsunami gaugesHigh‐resolution information about tsunami wave height is constructed from sparse observations based on the concept of super‐resolutionThe optimization process considering the existing observation network locations identifies their sufficiency or deficiency [ABSTRACT FROM AUTHOR]

This study presents a method for the detection of the most likely tsunami scenario among a set of possible scenarios using an observational wave sequence based on a sequential Bayesian update scheme. The proposed method consists of two phases: an offline preliminary learning phase and an online real‐time detection update phase. The innovation of this study is that proper orthogonal decomposition (POD) and Bayesian update are used together with an established tsunami simulation technique. In the offline reinforcement learning process, a series of tsunami simulations are carried out based on geophysically feasible scenarios, and the spatial modes of wave data calculated at predefined synthetic gauge locations are extracted through the application of POD. When a real tsunami event occurs and observational ocean data are obtained, the online process can then be performed as follows: using the stored spatial modes along with their component coefficients, pseudocoefficients are repeatedly estimated from the obtained wave data and used to sequentially update the most likely tsunami scenario according to the posterior probability through Bayesian update. A verification analysis is carried out to illustrate the procedure of the proposed method, and a validation analysis is conducted to demonstrate both the capabilities and applicability of the process with reasonable accuracy. A comprehensive discussion details the characteristic features of the proposed method in terms of the real‐time prediction of tsunami hazards and risks. Plain Language Summary: We present a real‐time tsunami scenario detection framework using sequential probability updates and a kind of unsupervised learning. We first carry out a series of tsunami simulations based on geophysically feasible earthquake scenarios. Then, the characteristics of simulated wave history data from predefined ocean gauges are learned by means of proper orthogonal decomposition (POD), that is, the commonalities ("spatial modes") among all tsunami scenarios are extracted. The scenario‐specific components ("component coefficients") obtained together with such spatial modes enable us to handle all learned scenarios. When a real tsunami event occurs, the extracted spatial modes are used to infer the specific components of the current event, called "pseudocoefficients", from the real‐time wave observations. By inputting these "pseudocoefficients" and the prelearned "component coefficient" into a Bayesian update scheme, the likelihood that each of the learned scenarios corresponds to the current event is sequentially evaluated. To demonstrate the specific procedure and its capabilities, validation analyses are conducted targeting the Nankai subduction zone. Our framework successfully detects the most likely scenarios, which have wave histories, maximum wave heights, and fault rupture patterns similar to those of the test scenarios, from the training dataset. Key Points: A sequential Bayesian update scheme for the detection of the most likely tsunami scenario using proper orthogonal decomposition is presentedTwo case studies targeting the Nankai Trough are carried out to demonstrate the procedure and capabilities of the proposed methodThe most likely tsunami scenarios are detected with 7 min of observational wave data from 71 synthetic gauge points [ABSTRACT FROM AUTHOR]

This study presents a method for determining the drag parameter in the 2D shallow water (SW) equation for flows through a coastal forest by conducting a series of 3D numerical simulations (3D NSs). Following the theory of multiscale modeling, an evaluation method procedure is proposed. We first prepare a local test domain that contains a sufficient number of trees to constitute part of a coastal forest. Then, 3D NSs are conducted in this test domain with various inflow conditions. Based on the corresponding results, the momentum losses over the test domain are converted into the drag parameter of the global SW equation. A response surface of the drag parameter is constructed as a function of the flow conditions. The stabilized finite element method is employed for both the local and the global NSs, and the phase‐field method is utilized to represent 3D free surfaces. Comparisons between the 2D SW calculation results and the 3D NS results are also performed to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

The goal of the Scientific Session: "Advances of International Collaboration on M9 Disaster Science" at the 2nd World Bosai Forum (WBF) in Sendai in November 2019 was to share progress on research projects and findings related to an M9 mega-disaster event, building on outcomes from a March 2019 collaborative workshop on M9 disaster science between research partners from the International Research Institute of Disaster Science (IRIDeS)/Tohoku University, University of Washington-Seattle (UW), and the Research Center for Integrated Disaster Risk Management (CIGIDEN). This paper reports on the presentations during the WBF Scientific Session, which shared updates and outputs of research collaborations from different disciplines, following the themes of risk-based planning, structural engineering, tsunami observation and early warning, and tsunami simulation and probabilistic tsunami risk assessment. This international and cross-disciplinary collaboration has led to the advancement of a number of specific research projects in different fields, as well as a robust network of researchers in the three countries. Based in coastal regions facing similar risks of massive earthquakes and tsunami in Japan, the United States, and Chile, it is hoped that ongoing and future collaboration within this network will continue to advance knowledge of disaster science and international disaster risk reduction. [ABSTRACT FROM AUTHOR]