The prediction of mid-winter and spring breakups of ice cover on Canadian rivers using a hybrid ontology-based and machine learning model. (February 2023)

• Record Type: Journal Article
• Authors: De Coste, Michael; Li, Zhong; Khedri, Ridha
• Is Part Of: Environmental modelling & software. Volume 160(2023)
• Publication Date: 2023-02
• Subjects: Mid-winter breakups -- Semantic modelling -- River ice -- Machine learning -- Ontology-based analytics; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105577
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Mid-winter breakups (MWBs) are an increasingly common event on Canadian rivers resulting in the early breakup of river ice cover, with complex and numerous drivers. This study focuses on the development of an MWB Ontology which allows the key data, events, and relationships in an ice season to be defined and analyzed. The MWB Ontology is applied to a national case study of 54 rivers in Canada. Through assessment of the MWB Ontology with network analysis techniques, a hybrid modelling framework coupling the MWB Ontology with machine learning is developed. The hybrid models produce greatly reduced errors in their forecasts of the timing breakup events, with the best performance being a mean absolute error of 12.47 days for MWBs and 10.68 days for spring breakup. The results demonstrate the utility of the MWB Ontology as a tool for collating data and a means for analysis and forecasting of these events. Highlights: Mid-Winter Breakups (MWBs) of river ice are becoming increasingly common and require complex data to predict. An MWB Ontology is developed to describe the domain of ice seasons with MWBs and organize related data. This Ontology is combined with machine learning in a hybrid model for forecasting of these events. The hybrid framework is successfully applied to the prediction of MWBs and spring breakups on a national scale.