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<p>This study presents the development of a Physics-Informed Neural Network (PINN) for predicting sediment transport rates, integrating physical laws governing sediment transport dynamics to improve prediction accuracy. The model was evaluated against traditional machine learning models, including Random Forest and Support Vector Regression (SVR), as well as empirical formulas, demonstrating superior performance with an average <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12665_2025_12295_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">\(R^2\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mi>R</mi> <mn>2</mn> </msup> </math></EquationSource> </InlineEquation> of 0.9573 and low error metrics. SHapley Additive exPlanations (SHAP) analysis revealed that dimensionless bed shear stress (<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12665_2025_12295_Article_IEq2.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="18" /> </InlineMediaObject> <EquationSource Format="TEX">\(\eta _b\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>η</mi> <mi>b</mi> </msub> </math></EquationSource> </InlineEquation>) and relative grain size (<i>Z</i>) were the most significant contributors to model predictions. A Graphical User Interface (GUI) was also developed to facilitate real-time interaction with the model, making advanced predictions accessible to hydrological engineers. The study underscores the potential of combining machine learning with physics-based constraints to enhance the predictive capabilities of sediment transport models, offering a practical tool for environmental management.</p>

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Physics-informed neural networks for predicting sediment transport in pressurized pipe flows

  • 期刊论文

  • 发表日期：

    2025年5月19日

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  • Rupesh Kumar Tipu,
  • Ruchika Bhakhar,
  • Kartik S. Pandya,
  • Vijay R. Panchal

摘要

This study presents the development of a Physics-Informed Neural Network (PINN) for predicting sediment transport rates, integrating physical laws governing sediment transport dynamics to improve prediction accuracy. The model was evaluated against traditional machine learning models, including Random Forest and Support Vector Regression (SVR), as well as empirical formulas, demonstrating superior performance with an average \(R^2\) R 2 of 0.9573 and low error metrics. SHapley Additive exPlanations (SHAP) analysis revealed that dimensionless bed shear stress ( \(\eta _b\) η b ) and relative grain size (Z) were the most significant contributors to model predictions. A Graphical User Interface (GUI) was also developed to facilitate real-time interaction with the model, making advanced predictions accessible to hydrological engineers. The study underscores the potential of combining machine learning with physics-based constraints to enhance the predictive capabilities of sediment transport models, offering a practical tool for environmental management.