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Abstract
As coastal population increases, so does the risk for social and economic losses under a changing climate. To assess future changes, much progress has been made towards developing shoreline numerical models, although producing reliable shoreline change predictions remains a challenge (Montaño 2020). Here we present a Deep Learning (DL) model to predict long-term shoreline evolution due to waves and large-scale atmospheric patterns. The model is based on two types of Artificial Neural Networks: Long-Short Term Memory (LSTM) networks and Convolutional Neural Networks (CNN).References
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Montaño, Coco, Antolínez, et al. (2020). Blind testing of shoreline evolution models. Scientific reports, vol 10(1), pp. 1–10.
Montaño, Coco, Cagigal, et al. (2021). A multiscale approach to shoreline prediction. Geophysical Research Letters, vol. 48(1).
Splinter, Turner, Davidson, et al. (2014). A generalized equilibrium model for predicting daily to interannual shoreline response. Journal of Geophysical Research, Earth Surface, vol. 119, pp. 1936–1958.
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Copyright (c) 2023 Ernesto-Eduardo Gómez-de la Peña, Giovanni Coco, Colin Whittaker, Jennifer Montano