DOI: 10.1111/mice.13074 ISSN: 1093-9687

A hybrid ontology‐based semantic and machine learning model for the prediction of spring breakup

Michael De Coste, Zhong Li, Ridha Khedri
  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Civil and Structural Engineering
  • Building and Construction


River ice breakups carry the potential for high flows and flooding and are of great interest to accurately predict. A challenge in forecasting these events is the management of the massive amounts of data associated with an ice season. This study couples ontological and machine learning models in a new hybrid modeling framework to predict spring breakup on a national scale. The Ice Season Ontology sorts the data and allows for a user‐friendly means of analyzing any ice season, providing insight on which variables are most and least central. With this, a refined variable selection is able to be made for machine learning models. The most successful developed model, a random forest, produced highly accurate forecasts when applied to a national scale case study, with a mean absolute error of 10.85 days and an R2 of .884. This new modeling framework provides a means for decision‐making support for river bound communities and a new methodology for modeling applications in other fields.

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