A Multi-Stage Physics-Verified Data Fusion and Bayesian-Optimized Machine Learning Framework for Multiclass Prediction of Ice Jam Floods and Release Waves: A Case Study of the Chaudière River, Québec

Accurate ice-period river monitoring is essential for characterizing ice jam floods (IJFs) and associated jam-release waves (JAVEs). However, physics-based and machine-learning (ML) approaches remain constrained by data scarcity, uncertainty, and incomplete observations. This study develops a physics-verified data fusion and preprocessing workflow combined with Bayesian-optimized ML classification (PV-DFBML) for six IJF/JAVE states at Beauceville on the Chaudière River. The workflow utilizes a three-stage data refinement pipeline. First, categorical fusion of remote sensing (Sentinel-1/2, Landsat-8, and MODIS) and meteorological data isolates physically relevant ice regimes. This is followed by spatiotemporal validation and gap-filling of water level data to preserve longitudinal hydraulic consistency, alongside multi-step anomaly screening (DBSCAN and CUSUM) cross-checked against upstream–downstream consistency to isolate sensor noise. Among five Bayesian-optimized classifiers, B-RF achieved the strongest performance under the common 5-fold validation framework, with BACC = 0.76, MCC = 0.89, and minority-class F1 = 0.83. Full-winter input records provided the strongest classification skill, while physics-verified preprocessing improved minority-event detection. Random undersampling modestly increased BACC and minority F1 to 0.79 and 0.84, respectively, while reducing MCC to 0.85, indicating a trade-off in imbalance treatment. More stringent leave-one-winter-out validation yielded mean BACC = 0.68 ± 0.10, MCC = 0.61 ± 0.13, and minority F1 = 0.65 ± 0.09, demonstrating greater difficulty in generalizing to unseen winters. Transfer to Saint-Joseph-de-Beauce without site-specific retraining further reduced BACC to 0.67, MCC to 0.71, and minority-class F1-score to 0.67. Overall, the results demonstrate the importance of physically consistent data refinement while highlighting limitations associated with rare-event representation and temporal and spatial generalization.