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- W4385348544 abstract "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 R 2 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." @default.
- W4385348544 created "2023-07-29" @default.
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- W4385348544 date "2023-07-27" @default.
- W4385348544 modified "2023-10-18" @default.
- W4385348544 title "A hybrid ontology‐based semantic and machine learning model for the prediction of spring breakup" @default.
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- W4385348544 doi "https://doi.org/10.1111/mice.13074" @default.
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