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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/nhess-2019-81
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-81
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 Apr 2019

Research article | 02 Apr 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

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

Tomas Beuzen1, Evan B. Goldstein2, and Kristen D. Splinter1 Tomas Beuzen et al.
  • 1Water Research Laboratory, School of Civil and Environmental Engineering, UNSW Sydney, NSW, Australia
  • 2Department of Geography, Environment, and Sustainability, University of North Carolina at Greensboro, Greensboro, NC, USA

Abstract. After decades of study and significant data collection of time-varying swash on sandy beaches, there is no single deterministic prediction scheme for wave runup that eliminates prediction error – even bespoke, locally tuned predictors present scatter when compared to observations. Scatter in runup prediction is meaningful and can be used to create probabilistic predictions of runup for a given wave climate and beach slope. This contribution demonstrates this using a data-driven Gaussian process predictor; a probabilistic machine learning technique. The runup predictor is developed using one year of hourly wave runup data (8328 observations) collected by a fixed LIDAR at Narrabeen Beach, Sydney, Australia. The Gaussian process predictor accurately predicts hourly wave runup elevation when tested on unseen data with a root mean-squared-error of 0.18 m and bias of 0.02 m. The uncertainty estimates output from the probabilistic GP predictor are then used practically in a deterministic numerical model of coastal dune erosion, which relies on a parameterization of wave runup, to generate ensemble predictions. When applied to a dataset of dune erosion caused by a storm event that impacted Narrabeen Beach in 2011, the ensemble approach reproduced ~ 85 % of the observed variability in dune erosion along the 3.5 km beach and provided clear uncertainty estimates around these predictions. This work demonstrates how data-driven methods can be used with traditional deterministic models to develop ensemble predictions that provide more information and greater forecasting skill when compared to a single model using a deterministic parameterization; an idea that could be applied more generally to other numerical models of geomorphic systems.

Tomas Beuzen et al.
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Tomas Beuzen et al.
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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.
Wave runup is important for characterizing coastal vulnerability to wave action, however it is...
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