Machine-learning approach for predicting the occurrence and timing of mid-winter ice breakups on canadian rivers

Abstract

The increasingly common occurrence of Mid-Winter Breakups (MWBs) in Canadian rivers, consisting of the early breakup of ice cover outside of the typical spring season, is a cause for concern. This study applied various data-driven modelling techniques to predict MWBs occurrence and timing with sufficient lead times on a national scale using a new Canadian River Ice Database (CRID) coupled with National Resources Canada gridded climate data. A two-level machine learning model structure was developed, with the first level model predicting MWB occurrence within a given period and the second level model predicting the timing of an MWB occurrence within that period. Machine learning techniques that can handle class imbalance were employed to address many of the issues inherent in rare event forecasting, including the implementation of data preprocessing, the selection of algorithms and performance metrics suited to rare events. Multiple configurations of both model levels, including variations on time series arrangement and input variables, were tested to select the optimal model structure. The best performing configuration, focussing on a biweekly time period, attained overall accuracies of 80.1% and 77.6% for the first and second level models respectively on the 452 MWBs in the CRID. In addition, probabilistic prediction results were analyzed to evaluate model uncertainty and robustness. This new modelling framework provides the first tool capable of predicting MWBs on a national scale, with easily extendable methodology to locations that have not yet experienced MWBs and can form the basis of vital decision-making support to affected communities.