Prediction of maximum scour depth downstream of bed sills using integrated machine learning algorithms - Guan, Dawei; Li, Zhanchen; Zheng, Haoran; Hong, Jian-Hao; Fazeres-Ferradosa, Tiago; Asumadu, Richard (Environmental modelling & software : with environment data news, 2025)
Accurate prediction of maximum relative scour depth (ys/Hs) is critical for hydraulic infrastructure resilience. This study advances scour depth prediction downstream of bed sills by establishing three ensemble models—Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and XGBoost—trained on a comprehensive dataset combining 328 standardized flume experiments (clear-water and live-bed conditions) with 73 field measurements. Validation using 29 field datasets from Maso River reveals MAE reductions of 32.5 %, 28.7 %, and 30.2 % for RF, GBDT, and XGBoost, respectively, compared to laboratory-trained models, translating to at least 31.6 % higher accuracy than traditional empirical approaches. Comprehensive sensitivity analysis identifies four dimensionless parameters as critical predictors, ranked by their relative importance to scour development: morphological transition coefficient (a1/Hs) > sediment sorting coefficient (a1/ΔD95) > weir spacing ratio (L/Hs) > channel slope (S0). By integrating lab and field data, this approach enhances scour prediction accuracy for fluvial risk management. •Combines 328 flume experiments and 73 field measurements, overcoming scale limitations of traditional empirical methods.•Morphological transition coefficient (a1/Hs) most influential, then sediment sorting (a1/ΔD95), weir spacing (L/Hs), slope (S0).•Ensemble (RF/GBDT/XGBoost) achieves R²=0.82, 36.6% lower RMSE vs empirical formulas, validated lab and field.