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Related Concept Videos

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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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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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Design Example: Measuring Distance Between Two Points with Obstructions01:10

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Related Experiment Video

Updated: Nov 14, 2025

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Reliability-based design and implementation of crow search algorithm for longitudinal dispersion coefficient

Alireza Ghaemi1, Tahmineh Zhian1, Bahareh Pirzadeh2

  • 1Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran.

Environmental Science and Pollution Research International
|March 8, 2021
PubMed
Summary

This study enhances river pollutant transport prediction using the crow search algorithm (CSA) with evolutionary polynomial regression (EPR). The CSA significantly improves the accuracy of longitudinal dispersion coefficient (LDC) modeling, reducing uncertainty in water quality assessments.

Keywords:
Artificial intelligenceCrow search algorithmLongitudinal dispersion coefficientMachine learningMonte Carlo simulationNatural riversReliability analysis

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Area of Science:

  • Environmental Science
  • Hydrology
  • Water Quality Management

Background:

  • Longitudinal Dispersion Coefficient (LDC) is crucial for river water quality assessment.
  • Existing LDC equations exhibit significant uncertainty due to complexity.
  • Accurate LDC modeling is essential for effective pollutant transport prediction.

Purpose of the Study:

  • To enhance the precision of LDC estimation using an advanced computational approach.
  • To model extensive geometrical and hydraulic data for improved LDC prediction.
  • To reduce uncertainty in water quality parameter estimations.

Main Methods:

  • Application of the Crow Search Algorithm (CSA) integrated with Evolutionary Polynomial Regression (EPR).
  • Modeling of a comprehensive dataset encompassing geometrical and hydraulic river characteristics.
  • Utilizing Monte Carlo simulation for reliability analysis and failure probability (Pf) determination.

Main Results:

  • CSA significantly improved EPR performance, achieving R2=0.8, Willmott's index=0.93, and Nash-Sutcliffe efficiency=0.77.
  • The proposed CSA model demonstrated superior accuracy in predicting LDC.
  • Reliability analysis showed reduced failure probability (Pf) with the CSA model.
  • A power function best predicted failure probability (R2=0.98).

Conclusions:

  • The CSA-EPR model offers a more precise and reliable method for estimating LDC in rivers.
  • This approach effectively reduces uncertainty in water quality modeling.
  • The findings support improved management strategies for riverine pollutant transport.