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Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

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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.
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Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

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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.
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Modeling and Similitude01:12

Modeling and Similitude

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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...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

46
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...
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Rapidly Varying Flow01:24

Rapidly Varying Flow

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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Related Experiment Video

Updated: Jul 1, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

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Published on: March 28, 2025

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Machine learning vs. statistical model for prediction modeling and experimental validation: Application in

Fengshi Guo1, Yangmin Ren1, Yongyue Zhou1

  • 1School of Civil, Environmental, and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, the Republic of Korea.

Journal of Hazardous Materials
|March 2, 2024
PubMed
Summary

This study introduces machine learning (ML) for designing permeable reactive barriers (PRBs) to remove arsenic from groundwater. ML accurately predicts barrier width, improving efficiency over traditional methods.

Keywords:
DesignGround waterMachine learningPermeable reactive barrierStatistical model

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Area of Science:

  • Environmental Engineering
  • Water Resource Management
  • Geochemistry

Background:

  • Permeable reactive barriers (PRBs) are effective in-situ groundwater remediation technologies.
  • PRB design hinges on critical factors like barrier width and reactive material selection.
  • Arsenic contamination in groundwater poses significant environmental and health risks.

Purpose of the Study:

  • To investigate beaded coal mine drainage sludge (BCMDS) as a reactive material for arsenic adsorption in PRBs.
  • To compare traditional design methods with machine learning (ML) approaches for determining PRB width.
  • To optimize ML models for accurate prediction of the mass transfer zone width (WMTZ).

Main Methods:

  • Utilized traditional column experiments and empirical formulas for PRB width determination.
  • Employed machine learning (ML), specifically the XGBoost algorithm, for predicting WMTZ using data from existing literature.
  • Validated ML predictions against experimentally derived WMTZ values and compared with multiple linear regression (MLR).

Main Results:

  • The XGBoost ML model achieved high accuracy in predicting WMTZ (R2 = 0.97, RMSE = 0.15).
  • ML predictions showed a low error rate of 7.04% when validated against experimental data.
  • Multiple linear regression (MLR) exhibited a significantly higher error rate of 39.43%.

Conclusions:

  • Machine learning offers superior accuracy and efficiency for PRB design compared to traditional methods, especially for complex contaminant scenarios.
  • ML effectively accounts for material, pollutant, and environmental factors in predicting barrier width.
  • Further development of ML models holds promise for broader application in groundwater remediation design.