Predicting Local Scour at Bridge Piers Using Probability-Distribution Machine Learning

The interactions of river flows with bridge piers generate strong vortices and locally accelerated velocities that disturb the bed and produce local scour. In this regard, approaches for predicting scour depth have evolved from empirical formulas derived from field and laboratory data to numerical models that solve the governing hydrodynamics and, more recently, machine-learning methods that learn patterns from data. Herein, we propose a model for predicting scour depth using NGBoost, which predicts full probability distributions rather than single values, thereby quantifying the predictive uncertainty and allowing for safety-oriented decisions through predictive intervals. To interpret the model, we apply Shapley additive explanations to identify the hydraulic and geomorphological variables that have the greatest influence on the predictions. When compared with representative empirical equations, the NGBoost model shows better accuracy, indicating enhanced performance for pier-scour prediction. Overall, NGBoost offers an uncertainty-aware interpretable framework to obtain a more reliable assessment of scour risk around bridge piers.