Related Concept Videos
01:10Propagation of Uncertainty from Systematic Error
1.2K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.2K
00:59Propagation of Uncertainty from Random Error
1.6K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.6K
00:54Uncertainty: Confidence Intervals
9.9K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
9.9K
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
246
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...
246
00:59Uncertainty: Overview
1.4K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.4K
01:30Typical Model Studies
574
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.
574
You might also read
Related Articles
Articles linked to this work by shared authors, journal, and citation graph.
Sort by
Same author
Current status of room temperature magnetic compensation in impurity-doped Mn<sub>4</sub>N epitaxial thin films.
Science and technology of advanced materials·2026
Same author
Methodological innovations in perfusable vascular networks: Advancing high-density tissue models.
Regenerative therapy·2026
Same author
Epitaxial Growth of ⟨100⟩-Oriented Ultrathin (∼4 nm) Continuous Pt Films on MgO(001) and SrTiO<sub>3</sub>(001) Substrates by Radio Frequency Magnetron Sputtering.
ACS applied materials & interfaces·2026
Related Experiment Video
Updated: Dec 29, 2025
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.5K
Bayesian tsunami fragility modeling considering input data uncertainty.
Raffaele De Risi1, Katsuichiro Goda1, Nobuhito Mori2
11Department of Civil Engineering, Queen's Building University Walk, University of Bristol, Bristol, BS8 1TR UK.
Stochastic Environmental Research and Risk Assessment : Research Journal
|February 7, 2020
Summary
This study develops empirical tsunami fragility curves using Bayesian methods, accounting for hazard data uncertainty. The binomial logistic method with un-binned data proved most effective for tsunami risk assessment.
Area of Science:
- Earthquake Engineering
- Natural Hazard Modeling
- Bayesian Statistics
Background:
- Tsunami events pose significant risks to coastal infrastructure.
- Accurate fragility curves are crucial for tsunami risk assessment.
- Uncertainty in hazard data can impact the reliability of fragility models.
Purpose of the Study:
- To develop empirical tsunami fragility curves using a Bayesian framework.
- To systematically account for and propagate uncertainty in tsunami hazard data.
- To compare different fragility modeling approaches and their influence on loss estimation.
Main Methods:
- Application of Bayesian framework for fragility curve development.
- Comparison of lognormal, binomial logistic, and multinomial logistic methods.
- Quantification and propagation of uncertainty from tsunami inundation/run-up datasets.
Main Results:
- The binomial logistic method with un-binned data is identified as the preferred model.
- Tsunami hazard data uncertainty is significant (coefficient of variation of 0.25).
- Neglecting input data uncertainty leads to overestimation of model uncertainty.
Conclusions:
- The developed tsunami fragility functions provide a robust tool for risk assessment.
- Accounting for input data uncertainty is essential for accurate tsunami loss estimation.
- Further research on multinomial logistic regression with un-binned data is recommended.