Do LSTM memory states reflect the relationships in reduced-complexity sandy shoreline models

Abstract

Equilibrium-based models are a transparent method of modelling shoreline change, though often too simplistic to capture complex dynamics. Conversely, deep learning methodologies offer greater predictive power at the expense of transparency. In this research we scrutinize the internal workings of an LSTM shoreline model. A regression-based probe is used to show that cell state vectors, responsible for past-to-future information flow, autonomously generate equilibrium-like information akin to the physics-based equilibrium term of the ShoreFor model, Ω e q . The variation in probe skill throughout training is tracked to show that at 5 of 6 transects, the LSTM was able to meaningfully acquire equilibrium information ( ΣΔR 2 = 0.3–0.6). The results of this work offer evidence that an LSTM may model shoreline change with internal methods that are consistent with the current understanding of coastal shoreline dynamics. These physically meaningful representations emphasize the importance of co-evolution between machine learning and physics-based approaches moving forward. • A Long Short-Term Memory (LSTM) neural network is devised to model shoreline change. • Cell state vectors are mapped to the equilibrium term of ShoreFor via a linear probe. • The LSTM solution in part reflects our current understanding of coastal dynamics. • Combining deep learning with a physics-based approach is a promising path forward.

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