A calibration workflow for coastal dune models

This is a Preprint and has not been peer reviewed.

Downloads

Download Preprint

Supplementary Files
Authors

Evan B Goldstein , Laura J Moore

Abstract

Numerical models of coastal dune growth encode feedbacks and nonlinearities between sediment transport and plant growth. The range of processes and tunable parameters involved make model calibration an important step when using models for prediction. In this paper we outline a method to calibrate models of coastal dune formation and describe the process from end to end. The first step is collection of both topographic and vegetation data at two time periods with photogrammetry using the technique of structure-from-motion. Using the first topographic and vegetation capture as the model initial condition, the free parameters in the model are then tuned by running the model many times and adjusting the free parameters with a genetic algorithm, a machine learning technique. A set of parameters is found that produces the lowest prediction error — and in this way the model is calibrated for local conditions. We outline this routine, provide an example, and direct the reader to the open source software developed as part of the workflow presented here, which can be used with other dune models and/or other datasets.

DOI

https://doi.org/10.31223/osf.io/cd87u

Subjects

Earth Sciences, Geomorphology, Physical Sciences and Mathematics

Keywords

Coastal Dunes, Genetic Algorithms, Kite Aerial Photography, Morphodynamics, Structure-from-Motion

Dates

Published: 2018-08-21 15:58

Older Versions
License

CC BY Attribution 4.0 International

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.