Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

91
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
91
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

171
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
171
Precipitation Gravimetry01:03

Precipitation Gravimetry

7.0K
Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
7.0K
Typical Model Studies01:30

Typical Model Studies

426
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
426
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Modeling and Similitude01:12

Modeling and Similitude

323
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
323

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Nonlinear combinatorial analysis of blood transcriptomes identifies PRKAR1A as a regulator of TDP-43 pathophysiology in amyotrophic lateral sclerosis.

Biology methods & protocols·2026
Same author

Subset binding enables detection of multimodal patient subgroup patterns and drug target discovery in idiopathic pulmonary fibrosis.

Briefings in bioinformatics·2026
Same author

Evidence of megathrust earthquakes and seismic supercycles in subtropical Japan from millennia-old coral microatolls.

Nature communications·2026
Same author

Clinically informed intermediate reasoning enables generalizable prostate cancer prognostication through machine learning in limited settings.

NPJ digital medicine·2025
Same author

Multi-horizon prediction of tropical cyclone intensity and its interpretability with temporal fusion transformer.

Scientific reports·2025
Same author

Current status and future direction of cancer research using artificial intelligence for clinical application.

Cancer science·2024

Related Experiment Video

Updated: Aug 28, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.4K

Machine learning-based tsunami inundation prediction derived from offshore observations.

Iyan E Mulia1,2, Naonori Ueda3,4, Takemasa Miyoshi3,5

  • 1Prediction Science Laboratory, RIKEN Cluster for Pioneering Research, Kobe, Japan. iyan.mulia@riken.jp.

Nature Communications
|September 19, 2022
PubMed
Summary

This study introduces a machine learning model for real-time tsunami inundation prediction using Japan's extensive tsunami observing system. The model offers rapid, accurate forecasts, significantly reducing computational costs compared to traditional methods.

More Related Videos

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

3.9K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.6K

Related Experiment Videos

Last Updated: Aug 28, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.4K
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

3.9K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.6K

Area of Science:

  • Earth Sciences
  • Computer Science
  • Oceanography

Background:

  • Tsunami prediction accuracy is crucial for coastal safety.
  • Conventional tsunami models require extensive computational resources and accurate source estimations.
  • Real-time tsunami inundation prediction remains a significant challenge.

Purpose of the Study:

  • To develop a machine learning-based method for real-time tsunami inundation prediction.
  • To leverage data from the world's largest tsunami observing system for improved forecasting.
  • To reduce computational costs and uncertainties associated with traditional tsunami modeling.

Main Methods:

  • Utilized data from 150 offshore stations in the Japan Trench.
  • Trained a machine learning model on 3093 hypothetical tsunami scenarios (Mw 8.0-9.1 megathrust and Mw 7.0-8.7 outer-rise earthquakes).
  • Tested the model against 480 unseen scenarios and 3 historical tsunami events.

Main Results:

  • The machine learning model achieved accuracy comparable to physics-based models.
  • Demonstrated a ~99% reduction in computational cost.
  • Successfully predicted tsunami inundation for seven coastal cities along the Sanriku coast.

Conclusions:

  • Machine learning offers a rapid and computationally efficient approach to real-time tsunami inundation prediction.
  • Direct use of offshore observations enhances forecast lead time and reduces source estimate uncertainties.
  • The developed method provides a viable alternative for improving tsunami early warning systems.