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Related Concept Videos

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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Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Uniform Depth Channel Flow: Problem Solving01:18

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Gradually Varying Flow01:29

Gradually Varying Flow

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Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
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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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Velocity and Acceleration in Steady and Unsteady Flow01:11

Velocity and Acceleration in Steady and Unsteady Flow

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In fluid mechanics, velocity and acceleration are key concepts for analyzing particle motion in both steady and unsteady flow. Consider a fluid particle moving along a pathline, where its velocity depends on its position and time. The particle's acceleration is obtained by differentiating the velocity with respect to time.
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Predicting Bulk Average Velocity with Rigid Vegetation in Open Channels Using Tree-Based Machine Learning: A Novel

D P P Meddage1, I U Ekanayake2, Sumudu Herath1

  • 1Department of Civil and Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka.

Sensors (Basel, Switzerland)
|June 24, 2022
PubMed
Summary

Predicting open channel flow velocity (UB) is challenging. Tree-based machine learning (ML) models, particularly XGBoost, accurately predict UB and friction factor (fS), with SHAP explaining feature importance.

Keywords:
bulk average velocityexplainable artificial intelligencerigid vegetationtree-based machine learning

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Area of Science:

  • Environmental fluid dynamics
  • Hydraulic engineering
  • Computational hydraulics

Background:

  • Predicting bulk-average velocity (UB) in open channels with rigid vegetation presents non-linear challenges.
  • Existing regression models lack transparency in feature importance and causality for UB predictions.

Purpose of the Study:

  • To develop and compare machine learning (ML) models for predicting UB and surface layer friction factor (fS) in vegetated open channels.
  • To utilize Shapley Additive exPlanation (SHAP) for interpreting ML model predictions and identifying key influencing factors.

Main Methods:

  • Employed tree-based ML models: decision tree, extra tree, and XGBoost.
  • Applied SHAP for model interpretability, analyzing feature importance and prediction dependencies.
  • Validated model performance against existing regression techniques.

Main Results:

  • XGBoost demonstrated superior predictive accuracy for UB (R = 0.984) and fS (R = 0.92) compared to traditional regression models.
  • SHAP analysis successfully elucidated the reasoning behind predictions and highlighted critical feature importance.
  • SHAP interpretations aligned with established understanding of complex flow behaviors, enhancing model credibility.

Conclusions:

  • XGBoost offers a robust and interpretable approach for predicting flow characteristics in vegetated open channels.
  • SHAP provides valuable insights into the drivers of flow behavior, increasing trust in ML-based predictions.
  • This study advances the application of ML in hydraulic engineering for more accurate and transparent flow modeling.