Articles | Volume 19, issue 10
Nat. Hazards Earth Syst. Sci., 19, 2295–2309, 2019

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

Nat. Hazards Earth Syst. Sci., 19, 2295–2309, 2019
Research article
 | Highlight paper
22 Oct 2019
Research article  | Highlight paper | 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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Cited articles

Atkinson, A. L., Power, H. E., Moura, T., Hammond, T., Callaghan, D. P., and Baldock, T. E.: Assessment of runup predictions by empirical models on non-truncated beaches on the south-east Australian coast, Coast. Eng., 119, 15–31, 2017. 
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, 2015. 
Berner, J., Achatz, U., Batté, L., Bengtsson, L., Cámara, A. D. L., Christensen, H. M., Colangeli, M., Coleman, D. R., Crommelin, D., Dolaptchiev, S. I., and Franzke, C. L.: Stochastic parameterization: Toward a new view of weather and climate models, B. Am. Meteorol. Soc., 98, 565–588, 2017. 
Beuzen, T. and Goldstein, E. B.: TomasBeuzen/BeuzenEtAl_2019_NHESS_GP_runup_model: First release of repo (Version 0.1), Zenodo,, 2019. 
Beuzen, T., Splinter, K. D., Turner, I. L., Harley, M. D., and Marshall, L.: Predicting storm erosion on sandy coastlines using a Bayesian network, in: Proceedings of Australasian Coasts & Ports: Working with Nature, 21–23 June 2017, Cairns, Australia, 102–108, 2017. 
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.
Final-revised paper