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

Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

8.2K
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...
8.2K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.1K
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...
1.1K
Uncertainty: Overview00:59

Uncertainty: Overview

1.3K
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.
1.3K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.4K
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...
1.4K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

143
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
143
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

98.1K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
98.1K

You might also read

Related Articles

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

Sort by
Same author

Effects of legume-wheat rotation patterns on wheat yield, quality, and soil microbial community in the North China Plain.

Frontiers in microbiology·2026
Same author

A cross-sectional study of autonomic dysfunction in patients with Parkinson's disease and vascular parkinsonism.

Acta neurologica Belgica·2026
Same author

Global daily CO<sub>2</sub> emissions from 1970 to 2024.

Scientific data·2026
Same author

Bilateral Nitrogen Interface Chemistry for Dendrite-Free Zinc-Iodine Batteries with Enhanced Four-Electron Redox Activity.

ACS nano·2026
Same author

Low latency global carbon budget indicates reduced land carbon sink in the year 2024.

National science review·2026
Same author

Spatiotemporal prediction and attribution of groundwater storage anomaly using enhanced hybrid deep learning modeling with uncertainty quantification.

Journal of environmental management·2026

Related Experiment Video

Updated: Nov 12, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.3K

Bayesian machine learning ensemble approach to quantify model uncertainty in predicting groundwater storage change.

Jina Yin1, Josué Medellín-Azuara2, Alvar Escriva-Bou3

  • 1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, Jiangsu, 210098, China; Yangtze Institute for Conservation and Development, Hohai Unversity, Nanjing, Jiangsu, 210098, China; Civil and Environmental Engineering, University of California, Merced, 95343, CA, USA.

The Science of the Total Environment
|March 19, 2021
PubMed
Summary

This study introduces a machine learning ensemble framework using Bayesian model averaging to predict groundwater storage changes. The approach enhances prediction reliability in agricultural regions, identifying groundwater pumping as a key driver.

Keywords:
Bayesian model averagingGroundwater storage changeIrrigation pumpingMachine learning ensembleUncertainty quantification

More Related Videos

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

13.6K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Related Experiment Videos

Last Updated: Nov 12, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.3K
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

13.6K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Area of Science:

  • Hydrology and Water Resources Management
  • Environmental Modeling
  • Machine Learning Applications

Background:

  • Complex nonlinear relationships exist between agricultural water demand, groundwater extraction, surface water delivery, climate, and groundwater storage.
  • Traditional physical models for groundwater are computationally intensive and often hindered by data scarcity.
  • Single machine learning models can underestimate prediction uncertainty and exhibit poor accuracy in complex hydrological systems.

Purpose of the Study:

  • To develop a novel machine learning-based groundwater ensemble modeling framework.
  • To integrate Bayesian model averaging for improved prediction reliability and uncertainty quantification.
  • To predict groundwater storage change in agricultural regions, specifically the San Joaquin River Basin.

Main Methods:

  • Developed three individual machine learning models: artificial neural network, support vector machine, and response surface regression.
  • Employed Bayesian model averaging to quantify uncertainty from model parameters and structures, producing forecast distributions.
  • Constructed ensemble models using weights and variances derived from individual model performance, validated at subregional and regional scales.

Main Results:

  • Machine learning models demonstrated remarkable predictive capability and high computational efficiency.
  • The ensemble model provided consistently reliable predictions across the San Joaquin River Basin compared to single-model approaches.
  • Groundwater pumping for agricultural irrigation was identified as the primary driver of groundwater storage change.

Conclusions:

  • The developed ensemble modeling framework offers a computationally efficient alternative to physical models for simulating groundwater response.
  • This approach is particularly valuable in agricultural regions with limited subsurface data.
  • The study highlights the effectiveness of ensemble modeling in improving the reliability of groundwater storage predictions.