Ensemble Machine Learning and Model Interpretability for Leakage Prediction in Hydraulic Tunnels

Abstract

Drill-and-blast tunnel construction in the Himalayan region often encounters complex geological and hydrogeological conditions, leading to significant water leakage that impacts project cost and stability. This study aims to enhance leakage prediction accuracy using ensemble machine learning techniques. Initial leakage estimates were made using Panthi’s semi-empirical approach for the Nilgiri-II Hydropower Project. A dataset comprising rock mass quality, topography, and permeability features was used to train four ensemble models: Bagging, Boosting (XGBoost), Voting, and Stacking. Among these, Bagging outperformed others with an R2 of 0.99, followed by Voting and Stacking (both R2 = 0.97). Partial Dependence Plots (PDP) and Individual Conditional Expectation (ICE) plots were used to interpret model predictions and identify key influencing features such as hydrostatic head (Hstatic), distance to the valley side (D), and joint parameters. These results demonstrate that ensemble learning, particularly bagging, is highly effective in modeling water leakage in challenging Himalayan tunnel environments.