Traditional vs. Machine-learning methods for forecasting sandy shoreline evolution using historic satellite-derived shorelines

Traditional vs. Machine-learning methods for forecasting sandy shoreline evolution using historic satellite-derived shorelines

Calkoen, Floris (Deltares; Vrije Universiteit Amsterdam)
Luijendijk, Arjen (TU Delft Coastal Engineering; Deltares)
Rivero, Cristian Rodriguez (Vrije Universiteit Amsterdam)
Kras, Etienne (Deltares)
Baart, F. (Deltares)

2021

Forecasting shoreline evolution for sandy coasts is important for sustainable coastal management, given the present-day increasing anthropogenic pressures and a changing future climate. Here, we evaluate eight different time-series forecasting methods for predicting future shorelines derived from historic satellite-derived shorelines. Analyzing more than 37,000 transects around the globe, we find that traditional forecast methods altogether with some of the evaluated probabilistic Machine Learning (ML) time-series forecast algorithms, outperform Ordinary Least Squares (OLS) predictions for the majority of the sites. When forecasting seven years ahead, we find that these algorithms generate better predictions than OLS for 54% of the transect sites, producing forecasts with, on average, 29% smaller Mean Squared Error (MSE). Importantly, this advantage is shown to exist over all considered forecast horizons, i.e., from 1 up to 11 years. Although the ML algorithms do not produce significantly better predictions than traditional time-series forecast methods, some proved to be significantly more efficient in terms of computation time. We further provide insight in how these ML algorithms can be improved so that they can be expected to outperform not only OLS regression, but also the traditional time-series forecast methods. These forecasting algorithms can be used by coastal engineers, managers, and scientists to generate future shoreline prediction at a global level and derive conclusions thereof.

Coastal engineering
Coastal oceanography
Data science
Deep learning
Forecasting shorelines
Machine learning
Sandy beaches
Shoreline-trajectories
Time-series forecasting

http://resolver.tudelft.nl/uuid:a555168c-f255-4ac0-abb5-e84217fcbff7

https://doi.org/10.3390/rs13050934

2072-4292

Remote Sensing, 13 (5), 1-21

Institutional Repository

journal article

© 2021 Floris Calkoen, Arjen Luijendijk, Cristian Rodriguez Rivero, Etienne Kras, F. Baart