Hydraulic Performance Modeling of Inclined Double Cutoff Walls Beneath Hydraulic Structures Using Optimized Ensemble Machine Learning - Mohamed Kamel Elshaarawy; Martina Zeleňáková; Asaad M. Armanuos (Scientific Reports)
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 2/θ 1), the depth ratio of downstream to upstream cutoff walls (d 2/d 1), and the relative downstream cutoff depth to the permeable layer depth (d 2/D). Outputs considered were the relative uplift force (U/U o ), the relative exit hydraulic gradient (i R /i Ro ), and the relative seepage discharge per unit structure length (q/q o ). 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 R2 values, specifically surpassing 0.95, 0.93, and 0.97 for U/U o , i R /i Ro , and q/q o , 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 o and i R /i Ro , while d 2/D played a crucial role in determining q/q o . Moreover, PDPs illustrated a positive linear relationship between L/B and U/U o , a V-shaped impact of d 2/d 1 on i R /i Ro and q/q o , and complex nonlinear interactions for θ 2/θ 1 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.