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
Abstract
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.