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This is a repository for the paper:

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

Tomas Beuzen<sup>1</sup>, Evan B. Goldstein<sup>2</sup>, Kristen D. Splinter<sup>1</sup>

<sup>1</sup>Water Research Laboratory, School of Civil and Environmental Engineering, UNSW Sydney, NSW, Australia

<sup>2</sup>Department of Geography, Environment, and Sustainability, University of North Carolina at Greensboro, Greensboro, NC, USA

Citation: Beuzen, T, Goldstein, E.B., Splinter, K.S. (In Review). Ensemble models from machine learning: an example of wave runup and coastal dune erosion, Natural Hazards and Earth Systems Science, SI Advances in computational modeling of geoprocesses and geohazards.

<img src="docs/figure.png" width="800" class="center" />

The folder paper_code contains a jupyter notebook that reproduces the GP runup predictor presented in the manuscript.

The folder LEH04model contains Python scripts for using the GP runup predictions from the 2011 storm in the Larson, Erikson, Hanson (2004) dune erosion model.

The folder data_repo contains data required to run the code.

For first-time users, it is recommended to install the Anaconda Distribution from: https://www.anaconda.com/distribution/.

The packages required to run the Jupyter notebooks are included in requirements_win64.txt. To install, do the following:

Jupyter notebooks will open in a new html window. Simply select the notebook to open it. To run notebook cells, use shift + enter.

For more information on using jupyter notebooks, see the documentation at https://jupyter.org/