Given the recent developments in machine-learning technology, its application has rapidly
progressed in various fields of earthquake seismology, achieving great success. Here, we …

Data mining and analysis are critical for preventing or mitigating natural hazards. However,
data availability in natural hazard analysis is experiencing unprecedented challenges due to …

The world's largest and densest tsunami observing system gives us the leverage to develop
a method for a real-time tsunami inundation prediction based on machine learning. Our …

Submarine landslides can generate destructive tsunamis. Yet their recurrence intervals and
tsunamigenic mechanisms are poorly understood, hampering quantification of global …

The integration of machine learning (ML) techniques in coastal engineering marks a
paradigm shift in how coastal processes are modeled and understood. While traditional …

After a seismic event, tsunami early warning systems (TEWSs) try to accurately forecast the
maximum height of incident waves at specific target points in front of the coast, so that early …

We present a novel method for coastal tsunami prediction utilizing a denoising autoencoder
(DAE) model, one of the deep learning algorithms. Our study focuses on the Tohoku coast …

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 …

Estimating coastal tsunami impact for early-warning or long-term hazard analysis requires
the calculation of inundation metrics such as flow-depth or momentum flux. Both applications …

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 …