Predicting vegetation-induced flow resistance remains a significant challenge due to the diverse and dynamic nature of river vegetation. Although numerous empirical models are available, they often fail to generalize across different environmental conditions, leading to inaccurate predictions. This study introduces a machine learning-based framework for predicting vegetation flow resistance, incorporating nine ML methods, including SVM, XGBoost, and BP. To improve predictive performance, optimization algorithms such as PSO, WSO, and RIME were applied. A comprehensive dataset of 490 samples across multiple scales was used to evaluate model accuracy, indicated: (1) The submergence ratio <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_91668_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="14" /> </InlineMediaObject> <EquationSource Format="TEX">\(\alpha\)</EquationSource> </InlineEquation> and Froude number Fr are the most sensitive parameters affecting Cd, while missing parameters such as vegetation density <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_91668_Article_IEq2.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\lambda\)</EquationSource> </InlineEquation> and blockage ratio <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_91668_Article_IEq3.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\beta\)</EquationSource> </InlineEquation> significantly reduce accuracy; (2) XGBoost outperforms other models, achieving the highest predictive accuracy (R2 = 0.9552); (3) The framework remains stable across six parameter deficiency scenarios, with XGBoost maintaining R2 > 0.85 in all cases. In conclusion, this study highlights the transformative potential of the proposed predictive framework in overcoming the long-standing challenges of estimating flow resistance in vegetated channels. It provides valuable insights for sustainable river management, bolsters restoration efforts, and enhances predictive accuracy in complex, dynamic environments.

Predictive framework of vegetation resistance in channel flow

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2025年3月4日

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Fengcong Jia,
Weijie Wang,
Yu Han,
Jiayu Du,
Yue Zhang,
Zihan Liu,
Hairong Gao

摘要

Predicting vegetation-induced flow resistance remains a significant challenge due to the diverse and dynamic nature of river vegetation. Although numerous empirical models are available, they often fail to generalize across different environmental conditions, leading to inaccurate predictions. This study introduces a machine learning-based framework for predicting vegetation flow resistance, incorporating nine ML methods, including SVM, XGBoost, and BP. To improve predictive performance, optimization algorithms such as PSO, WSO, and RIME were applied. A comprehensive dataset of 490 samples across multiple scales was used to evaluate model accuracy, indicated: (1) The submergence ratio \(\alpha\) and Froude number F_r are the most sensitive parameters affecting C_d, while missing parameters such as vegetation density \(\lambda\) and blockage ratio \(\beta\) significantly reduce accuracy; (2) XGBoost outperforms other models, achieving the highest predictive accuracy (R² = 0.9552); (3) The framework remains stable across six parameter deficiency scenarios, with XGBoost maintaining R² > 0.85 in all cases. In conclusion, this study highlights the transformative potential of the proposed predictive framework in overcoming the long-standing challenges of estimating flow resistance in vegetated channels. It provides valuable insights for sustainable river management, bolsters restoration efforts, and enhances predictive accuracy in complex, dynamic environments.