Jove
Visualize
Contact Us

Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

155
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.
155
Modeling and Similitude01:12

Modeling and Similitude

124
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...
124
Levels of Use of a GIS01:29

Levels of Use of a GIS

18
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
18
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

79
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
79
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

26
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
26
Plane Potential Flows01:23

Plane Potential Flows

173
Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
173

You might also read

Related Articles

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

Sort by
Same author

A framework for objectively comparing competing invasion percolation models based on highly-resolved image data.

PloS one·2026
Same author

On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data.

Entropy (Basel, Switzerland)·2024
Same author

Robust Data Worth Analysis with Surrogate Models.

Ground water·2021
Same author

A Maximum-Entropy Method to Estimate Discrete Distributions from Samples Ensuring Nonzero Probabilities.

Entropy (Basel, Switzerland)·2020
Same author

Defensible Model Complexity: A Call for Data-Based and Goal-Oriented Model Choice.

Ground water·2017
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 Experiment Video

Updated: May 10, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

7.9K

Comparing Physics-Based, Conceptual and Machine-Learning Models to Predict Groundwater Levels by BMA.

Thomas Wöhling, Alvaro Oliver Crespo Delgadillo1, Moritz Kraft1

  • 1Chair of Hydrology, Dresden University of Technology (TUD), 01069, Dresden, Germany.

Ground Water
|April 21, 2025
PubMed
Summary
This summary is machine-generated.

Comparing groundwater models, this study found that while data-driven approaches are competitive for individual well predictions, physics-based models like MODFLOW excel when forecasting for multiple wells simultaneously.

More Related Videos

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

1.2K
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

13.2K

Related Experiment Videos

Last Updated: May 10, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

7.9K
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

1.2K
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

13.2K

Area of Science:

  • Hydrogeology and Water Resource Management
  • Computational Hydrology
  • Machine Learning in Environmental Science

Background:

  • Groundwater level data are crucial for aquifer management and operational forecasting.
  • Various modeling approaches exist, ranging from physics-based to data-driven methods.
  • Assessing the performance of these diverse models is essential for effective water resource management.

Purpose of the Study:

  • To compare the predictive performance of a 3D groundwater flow model (MODFLOW) against eigen-, transfer-function, and machine learning models (ML) for groundwater level forecasting.
  • To evaluate if physics-based models offer advantages in integrated predictions across multiple wells compared to individually optimized data-driven models.
  • To determine the suitability of different model classes for operational groundwater level prediction tasks.

Main Methods:

  • Evaluated MODFLOW, eigenmodel, transfer-function model, multi-layer perceptron, long short-term memory, and random forest models.
  • Tested models on four groundwater level time series from the Wairau Aquifer, New Zealand.
  • Employed Bayesian model averaging to assess ensemble forecasting performance and derive model weights.

Main Results:

  • Data-driven models demonstrated competitiveness for predicting individual groundwater wells, even outside calibration ranges.
  • No single model universally outperformed others for all prediction scenarios.
  • Physics-based MODFLOW was favored by Bayesian model averaging for simultaneous prediction of all four wells, indicating its strength in integrated system representation.

Conclusions:

  • Data-driven models are viable for localized, single-well groundwater level predictions.
  • Physics-based models retain significant advantages for integrated aquifer management and multi-well forecasting.
  • Ensemble forecasting using Bayesian model averaging effectively combines diverse model strengths, highlighting the continued importance of physics-based approaches.