From Concept to Application: Machine Learning for Near-Real-Time River Ice Breakup Prediction Using SAR and Meteorological Data

Abstract. Accurate, reliable, and early-warning forecasts of river ice breakup are essential for flood risk mitigation and public safety, particularly in relation to river transportation and ice road operations. Synthetic Aperture Radar (SAR) satellite imagery has been widely utilized for monitoring river ice conditions due to its sensitivity to surface roughness and dielectric properties. This study advances traditional SAR applications and, to our knowledge, presents the first model that directly incorporates SAR data as input within a machine learning (ML) framework for river ice breakup prediction. The method leverages the correlation between SAR backscatter dynamics and the onset of surface melt. The model was evaluated using leave-one-out cross-validation, achieving an overall accuracy of 92%, an F1-score of 0.91, a Kappa coefficient of 0.84, and a mean absolute error (MAE) of less than 6 days for both the two- and three-week forecasts. The algorithm was also implemented in near-real-time operational settings, demonstrating strong performance with MAE values ranging from zero to four days across different river segments. The approach was further tested on an independent site, where it maintained robust predictive skill. The newly developed method shows strong potential for two- and three-week forecasting of river ice breakup, offering a scalable, cost-effective, and operationally viable tool for management and early warning applications.