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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
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.
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.
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