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.

Data sets

TomasBeuzen/BeuzenEtAl_2019_NHESS_GP_runup_model: First release of repo (Version 0.1) T. Beuzen and E. B. Goldstein https://doi.org/10.5281/zenodo.3401739

Model code and software

TomasBeuzen/BeuzenEtAl_2019_NHESS_GP_runup_model: First release of repo (Version 0.1) T. Beuzen and E. B. Goldstein https://doi.org/10.5281/zenodo.3401739

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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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