Predictive framework of vegetation resistance in channel flow

Area of Science:

• Environmental Science
• Hydrology
• Computational Fluid Dynamics

Background:

• Predicting flow resistance in vegetated rivers is complex due to vegetation's dynamic nature.
• Existing empirical models often lack generalizability across diverse environmental conditions, leading to inaccurate predictions.
• Accurate flow resistance estimation is vital for effective river management and ecological restoration.

Purpose of the Study:

• To develop and evaluate a machine learning (ML) framework for predicting vegetation-induced flow resistance.
• To identify key parameters influencing flow resistance and assess the impact of data deficiencies.
• To enhance the accuracy and reliability of flow resistance predictions in vegetated channels.

Main Methods:

• Incorporated nine ML methods (e.g., SVM, XGBoost, BP) and three optimization algorithms (PSO, WSO, RIME).
• Utilized a comprehensive dataset of 490 samples across multiple scales for model training and evaluation.
• Assessed model performance under six parameter deficiency scenarios.

Main Results:

• Submergence ratio (α) and Froude number (Fr) were identified as the most sensitive parameters affecting drag coefficient (Cd).
• XGBoost demonstrated superior predictive accuracy (R² = 0.9552) compared to other ML models.
• The ML framework exhibited stability, with XGBoost maintaining R² > 0.85 even with missing parameters.

Conclusions:

• The proposed ML framework offers a robust solution for predicting vegetation flow resistance, overcoming limitations of empirical models.
• Accurate flow resistance prediction using ML provides valuable insights for sustainable river management and restoration.
• This approach enhances predictive capabilities in complex, dynamic vegetated aquatic environments.