Accuracy–Cost–Robustness Trade-Offs in Rigid Water Column Model-Trained Machine Learning Surrogates for Transient Leakage Prediction During Pressure-Reducing Valve Manoeuvres

Rapid leakage prediction during pressure-reducing valve manoeuvres requires models that reproduce inertial hydraulic effects at low computational cost. This study proposes a reproducible surrogate-modelling framework in which transient leakage responses are generated with an extended rigid water column model incorporating time-dependent valve resistance and then used to train machine learning regressors. Twenty-eight regression models were evaluated using four SCADA-oriented predictors: time, inlet flow, upstream pressure head, and valve position. Model selection followed an accuracy–cost–predictive-stability assessment that considered predictive error, training time, inference speed, model size, and behaviour under near-domain, boundary-unseen, and extreme extrapolation scenarios. Gaussian process regression achieved the lowest in-domain errors, with test root mean square error values of 0.0017–0.0019 L/s, but required training times above 21,000 s and inference speeds below 700 observations/s. Bagged Trees provided the most balanced option for PRV-operation screening within the represented hydraulic domain, combining low prediction error, high inference capacity, and stable behaviour within the evaluated near-domain and boundary-unseen range. The P3 extrapolation test showed that prediction beyond the represented hydraulic envelope requires scenario-library expansion and model reassessment. The framework supports rapid valve-operation screening and numerical assessment of RWCM-generated transient leakage responses, while field or SCADA-supported use requires local calibration and validation.