Articles | Volume 19, issue 10
Nat. Hazards Earth Syst. Sci., 19, 2295–2309, 2019
https://doi.org/10.5194/nhess-19-2295-2019

Special issue: Advances in computational modelling of natural hazards and...

Nat. Hazards Earth Syst. Sci., 19, 2295–2309, 2019
https://doi.org/10.5194/nhess-19-2295-2019

Research article 22 Oct 2019

Research article | 22 Oct 2019

Ensemble models from machine learning: an example of wave runup and coastal dune erosion

Tomas Beuzen et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (06 Sep 2019) by Randall LeVeque
AR by Tomas Beuzen on behalf of the Authors (07 Sep 2019)  Author's response    Manuscript
ED: Publish as is (11 Sep 2019) by Randall LeVeque
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Short summary
Wave runup is important for characterizing coastal vulnerability to wave action; however, it is complex and uncertain to predict. We use machine learning with a high-resolution dataset of wave runup to develop an accurate runup predictor that includes prediction uncertainty. We show how uncertainty in wave runup predictions can be used practically in a model of dune erosion to make ensemble predictions that provide more information and greater predictive skill than a single deterministic model.
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