Machine learning-based prediction for airflow velocity in unpressured water-conveyance tunnels

Spillway and drainage tunnels have an open-channel flow pattern when operating under unpressured condition, above which air flow is driven and resisted by water flow, wall friction, and pressure difference. Unpressured tunnels present many airflow-related safety and environmental issues, including water flow fluctuation, gate vibration, shaft cover blow-off, and odor emission; therefore, it is valuable to study and predict their airflow velocity. Given the difficulty in accurate prediction of airflow velocity in unpressured tunnels and complicated influences of hydraulic, structural, and boundary pressure parameters, this study focuses on establishing high-performance prediction models and understanding the importance and independent and coupled influences of each parameter using machine learning. It is found that the water Froude number, ratio of free-surface width to unwetted perimeter, relative ventilation area, and relative tunnel length are four key parameters. By including these parameters in the input parameter combination, the machine learning models can well predict the airflow velocity in unpressured tunnels, achieving significantly higher performance than the existing empirical and theoretical models. Among these models, the models built by Random Forest and XGBoost demonstrate best performance with R2 ≥ 0.911. The interpretability analysis reveals the highest importance of the water Froude number and the ratio of free-surface width to unwetted perimeter, increases in which generally result in enhancement of the airflow velocity. The water Froude number plays a dominant role when it is ≤11.5, and a continuous increase exhibits a significantly marginal effect. The relative ventilation area and relative length of tunnels have close importances, with an increase in either generally promoting the airflow velocity. To help researchers and engineers unfamiliar with machine learning to easily and accurately predict the airflow velocity in unpressured tunnels, GPlearn algorithm is employed to establish explicit expressions, which is validated to have good performance with R2 close to 0.900.