Hydraulic Performance Modeling of Inclined Double Cutoff Walls Beneath Hydraulic Structures Using Optimized Ensemble Machine Learning.
Journal:
Scientific reports
Published Date:
Jul 29, 2025
Abstract
This study investigates the effectiveness of inclined double cutoff walls installed beneath hydraulic structures by employing five machine learning models: Random Forest (RF), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). A comprehensive dataset of 630 samples was gathered from previous studies, including key input variables such as the relative distance between the cutoff wall and the structure's apron width (L/B), the inclination angle ratio between downstream and upstream cutoffs (θ/θ), the depth ratio of downstream to upstream cutoff walls (d/d), and the relative downstream cutoff depth to the permeable layer depth (d/D). Outputs considered were the relative uplift force (U/U), the relative exit hydraulic gradient (i/i), and the relative seepage discharge per unit structure length (q/q). The dataset was split with a 70:30 ratio for training and testing. Hyperparameter optimization was conducted using Bayesian Optimization (BO) coupled with five-fold cross-validation to enhance model performance. Results showed that the CatBoost model demonstrated superior performance over other models, consistently yielding high R values, specifically surpassing 0.95, 0.93, and 0.97 for U/U, i/i, and q/q, respectively, along with low RMSE scores below 0.022, 0.089, and 0.019 for the same variables. A feature importance analysis is conducted using SHapley Additive exPlanations (SHAP) and Partial Dependence Plot (PDP). The analysis revealed that L/B was the most influential predictor for U/U and i/i, while d/D played a crucial role in determining q/q. Moreover, PDPs illustrated a positive linear relationship between L/B and U/U, a V-shaped impact of d/d on i/i and q/q, and complex nonlinear interactions for θ/θ across all target variables. Furthermore, an interactive Graphical User Interface (GUI) was developed, enabling engineers to efficiently predict output variables and apply model insights in practical scenarios.
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