https://github.com/TomasBeuzen/BeuzenEtAl_2019_NHESS_GP_runup_model

Repository for the Beuzen et al (2019) paper "Ensemble models from machine learning: an example of wave runup and coastal dune erosion."
https://github.com/TomasBeuzen/BeuzenEtAl_2019_NHESS_GP_runup_model

Last synced: about 1 month ago
JSON representation

Repository for the Beuzen et al (2019) paper "Ensemble models from machine learning: an example of wave runup and coastal dune erosion."

• Host: GitHub
• URL: https://github.com/TomasBeuzen/BeuzenEtAl_2019_NHESS_GP_runup_model
• Owner: TomasBeuzen
• License: mit
• Created: 2018-08-14T17:31:27.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-11-12T14:59:29.000Z (about 5 years ago)
• Last Synced: 2024-06-11T16:44:02.929Z (6 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 5.07 MB
• Stars: 5
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-coastal - TomasBeuzen/BeuzenEtAl_GP_Paper - Process machine learning implementation of wave runup prediction. (By topic / Wave modelling)

README

        
[![DOI](https://zenodo.org/badge/144752081.svg)](https://zenodo.org/badge/latestdoi/144752081)

This is a repository for the paper:

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

### Tomas Beuzen1, Evan B. Goldstein2, Kristen D. Splinter1

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

 

**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.



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.

* The file `lidar_runup_data_for_GP_training.csv` contains the 416 runup samples used to develop the GP predictor.

* The file `lidar_runup_data_for_GP_testing.csv` contains 50 additional runup samples for the purpose of testing the GP predictor.

* Additional data including runup observations and dune erosion observations used with the manuscript are not yet publicly available. Please email the lead author for details.

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:

* Open a Windows Command Processor 

* Create a new environment using: `conda create -n "envGP" --file requirements_win64.txt`

* Activate that environment using: `conda activate envGP`

* Change to the directory where the notebook is located, e.g: `cd C:\Users\TomasBeuzen\BeuzenEtAl_2019_NHESS_GP_runup_model\paper_code`

* Run jupyter notebooks using: `jupyter notebook`

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/