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Weir: Problem Solving01:26

Weir: Problem Solving

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Water flow in open channels is often measured using hydraulic structures such as weirs, which allow precise calculation of discharge. In a rectangular channel, flow rates are measured using three types of weirs: rectangular sharp-crested, triangular sharp-crested, and broad-crested. The weir head is set at a fixed height above the channel bottom, simplifying calculations and enabling the relationship between depth and flow rate to be analyzed.For the rectangular sharp-crested weir, the flow...
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Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

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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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Weir01:24

Weir

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A weir is a hydraulic structure designed to partially obstruct an open channel, enabling precise control and measurement of water flow. By forcing water to flow over or through it, a weir allows for accurate determination of discharge rates, making it an essential tool in water resource management. These structures are extensively used in regulating river flows, irrigation systems, and flood control channels.Types of Weirs and Their FeaturesWeirs are categorized primarily into sharp-crested and...
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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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Underflow Gates01:30

Underflow Gates

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Underflow gates are vital for controlling water flow in irrigation canals. The three main types of underflow gates — vertical, radial, and drum gates — serve different purposes while ensuring effective flow management. Vertical gates move up and down, generating a free-flowing water jet; radial gates pivot to regulate the flow; and drum gates rotate for precise adjustments. The flow through these gates is influenced by downstream conditions, resulting in free or drowned outflow.Free and...
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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: Jan 16, 2026

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure
07:15

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure

Published on: April 25, 2025

973

Machine learning-based estimation of discharge coefficient for semicircular labyrinth weirs.

Akbar Asgharzadeh-Bonab1, Sajad Bijanvand2, Abbas Parsaie3

  • 1Department of Science and Technology Studies, AJA Command and Staff University, Tehran, Iran.

Scientific Reports
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

Advanced machine learning models accurately predict discharge coefficients in Semicircular Labyrinth Weirs (SCLWs). The TabNet-MFO model demonstrated superior performance in testing, offering improved hydraulic design predictions.

Keywords:
Advanced machine learningDischarge coefficientHybrid optimization modelSemicircular labyrinth weirsSensitivity analysis

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Area of Science:

  • Hydraulics and Fluid Mechanics
  • Computational Engineering
  • Artificial Intelligence

Background:

  • Accurate discharge coefficient (Cd) prediction is vital for hydraulic structure design.
  • Semicircular Labyrinth Weirs (SCLWs) require precise hydraulic performance estimation.
  • Traditional methods for Cd estimation have limitations.

Purpose of the Study:

  • To develop and evaluate advanced Machine Learning (ML) models for estimating Cd in SCLWs.
  • To compare the performance of various ML models including TabNet-MFO, ELM-JFO, LightGBM, and DT.
  • To identify key factors influencing Cd predictions in SCLWs.

Main Methods:

  • Exploration of ML models: Tabular Neural Network (TabNet) with Moth Flame Optimization (MFO), Extreme Learning Machine (ELM) with Jaya and Firefly Algorithms (JFO), Decision Tree (DT), and Light Gradient Boosting Machine (LightGBM).
  • Sensitivity analysis using Explainable Boosting Machine (EBM) and SHapley Additive exPlanations (SHAP) to determine influential parameters.
  • Performance evaluation using statistical indicators: R², RMSE, sMAPE, SI, WMAPE, Taylor diagrams, and Performance Index (PI).

Main Results:

  • The ratio of upstream flow depth to weir height (h/P) was identified as the most significant factor influencing Cd.
  • In training, ELM-JFO showed the best performance (PI=166, E'=0.0052), followed closely by TabNet-MFO (PI=142, E'=0.0068).
  • In testing, TabNet-MFO achieved the highest accuracy (PI=81.92, E'=0.0118), outperforming ELM-JFO, LightGBM, and DT.

Conclusions:

  • Hybrid and interpretable ML techniques, particularly TabNet-MFO, offer a reliable alternative for Cd estimation in SCLWs.
  • The developed ML models significantly improve flow prediction accuracy compared to traditional methods.
  • This research supports enhanced hydraulic structure design through advanced computational approaches.