Earthquake and tsunami predictions comprise huge uncertainties, thus necessitating probabilistic assessments for the design of defense facilities and urban planning. In recent years, computer development has advanced probabilistic tsunami hazard assessments (PTHAs), where hazard curves show the exceedance probability of the maximum tsunami height. However, owing to the lack of historical and geological tsunami records, this method is generally insufficient for validating the estimated hazard curves. The eastern coast of Shikoku in the Nankai subduction zone, Japan, is suitable for validation because tsunami records from historical Nankai Trough earthquakes are available. This study evaluated PTHAs by comparing the tsunami hazard curves and exceedance frequencies of historical Nankai Trough tsunamis. We considered 3480 earthquake scenarios representing the rupture patterns of past Nankai earthquakes and calculated all tsunamis. The probability of earthquake occurrence was based on the Gutenberg–Richter law. We considered uncertainty in tsunami calculations with astronomical tide variations. The estimated tsunami hazard curves are consistent with the exceedance frequencies obtained from historical tsunamis. In addition, sensitivity tests indicate the significance of the earthquake slip heterogeneity and tsunami defense facilities in PTHAs. We also extended the PTHAs to tsunami inundation maps in high resolution and proposed an effective new method for reducing the tsunami computation load. Graphical Abstract

The detailed understanding of tsunami hazard risk using numerical simulations requires a numerical model that can accurately predict tsunami inundation phenomena on land. In such models, the structural effects are indirectly considered using the variation of bottom roughness as a proxy for the differences in building densities. Only a few studies have conducted intermodel tests to investigate tsunami inundation in complex coastal urban cities. During the tsunami analysis hackathon held in September 2020, eight research groups met to have a detailed discussion on the current urban inundation problems. In this study, we conducted an intermodel comparison of the numerical tsunami models, using the data from physical experiments that were performed on a detailed urban model. Our objective was to investigate the necessary conditions of an accurate numerical model based that can ensure high reproducibility and practicality. It was confirmed that the accuracy of topographic data is an important parameter for tsunami inundation simulations in complex urban areas. Based on the computational cost and accuracy, we suggest that a resolution of 1 cm of topographic data is a sufficient condition for tsunami inundation simulations on 1/250 scale model.

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. Graphical Abstract