Within the context of climate change, understanding and predicting coastal change is of the foremost importance to protect coastal communities and coastal assets. This study analyzes field data from 125 locations along the Morecambe coastline, consisting of beach transects collected twice a year for more than a decade (2007 to 2022). To model the sediment volume changes observed along the Morecambe coastline, this study proposes a two-stage machine learning model that incorporates beach behavior classification and deep learning techniques to predict changes in sediment volumes along coastal environments. The first stage of the model, developed using a random forest classifier, classifies beach behavior into four categories: eroding, accreting, stable, or undergoing short-term fluctuations. The second stage of the model developed using LSTM and sequence-to-sequence models, uses the output of the first stage to predict the available sediment volume after erosion/accretion. LSTM model achieved a testing regression of 0.9961 for one-step-ahead (6 months) predictions of sediment volume time series, while sequence-to-sequence model achieved the testing regression of 0.9950 for three-time-ahead (1.5 years) predictions and 0.9916 for ten-time-step-ahead (5 years) prediction.

Coastal protection is of paramount importance because erosion and flooding affect millions of people living along the coast and can largely influence countries’ economy. The implementation of nature-based solutions for coastal protection, such as sand engines, has become more popular due to these interventions’ adaptability to climate change. This study explores synergies between AI and hydro-morphodynamic models for the creation of efficient decision-making tools for the choice of optimal sand engines configurations. Specifically, we investigate the use of long-short-term memory (LSTM) models as predictive tools for the morphological evolution of sand engines. We developed different LSTM models to predict time series of bathymetric changes across the sand engine as well as the time-decline in the sand engine volume as a function of external forces and intervention size. Finally, a MATLAB framework was developed to return LSTM model results based on users’ inputs about sand engine size and external forcings.

As climate-driven risks for the world’s coastlines increase, understanding and predicting morphological changes as well as developing efficient systems for coastal forecast has become of the foremost importance for adaptation to climate change and informed coastal management choices. Artificial Intelligence, especially deep learning, is a powerful technology that has been rapidly evolving over the last couple of decades and can offer new means of analysis for the coastal science field. Yet, the potential of these technologies for coastal geomorphology remains relatively unexplored with respect to other scientific fields. This article investigates the use of Artificial Neural Networks and Bayesian Networks in combination with fully coupled hydrodynamics and morphological models (Delft3D) for predicting morphological changes and sediment transport along coastal systems. Two sets of deep learning models were tested, one set relying on localized modelling outputs or localized data sources and one set having reduced dependency from modeling outputs and, once trained, solely relying on boundary conditions and coastline geometry. The first set of models provides regression values greater than 0.95 and 0.86 for training and testing. The second set of reduced-dependency models provides regression values greater than 0.84 and 0.76 for training and testing. Both model types require a running time of the order of minutes, compared to the several hours of running times of the hydrodynamic models. Our results highlight the potential of deep learning and statistical models for coastal applications.

Embayed beaches separated by irregular rocky headlands represent 50% of global shorelines. Quantification of inputs and outflows via headland bypassing is necessary for evaluating long-term coastal change. Bypassing rates are predictable for idealised headland morphologies; however, it remains to test the predictability for realistic morphologies, and to quantify the influence of variable morphology, sediment availability, tides and waves-tide interactions. Here we show that headland bypassing rates can be predicted for wave-dominated conditions, and depend upon headland cross-shore length normalised by surf zone width, headland toe depth and spatial sediment coverage. Numerically modelled bypassing rates are quantified for 29 headlands under variable wave, tide and sediment conditions along 75km of macrotidal, embayed coast. Bypassing is predominantly wave-driven and nearly ubiquitous under energetic waves. Tidal elevations modulate bypassing rates, with greatest impact at lower wave energies. Tidal currents mainly influence bypassing through wave-current interactions, which can dominate bypassing in median wave conditions. Limited sand availability off the headland apex can reduce bypassing by an order of magnitude. Bypassing rates are minimal when cross-shore length > 5 surf zone widths. Headland toe depth is an important secondary control, moderating wave impacts off the headland apex. Parameterisations were tested against modelled bypassing rates, and new terms are proposed to include headland toe depth and sand coverage. Wave-forced bypassing rates are predicted with mean absolute error of a factor 4.4. This work demonstrates wave-dominated headland bypassing is amenable to parameterisation and highlights the extent to which headland bypassing occurs with implications for embayed coasts worldwide.

Waves and tidal currents resuspend and transport shelf sediments, influencing sediment distributions and bedform morphology with implications for various topics including benthic habitats, marine operations, and marine spatial planning. Shelf-scale assessments of wave-tide-dominance of sand transport tend not to fully include wave-tide interactions (WTI), which non-linearly enhance bed shear stress and apparent roughness, change the current profile, modulate wave forcing, and can dominate net sand transport. Assessment of the relative contribution of WTI to net sand transport requires computationally/ labour intensive coupled numerical modelling, making comparison between regions or climate conditions challenging. Using the Northwest European Shelf, we show the dominant forcing mode and potential magnitude of net sand transport is predictable from readily available, uncoupled wave, tide and morphological data in a computationally efficient manner using a k-Nearest Neighbour algorithm. Shelf areas exhibit different dominant forcing modes for similar wave exceedance conditions, relating to differences in depth, grain size, tide range, and wave exposure. WTI dominate across most areas in energetic combined conditions. Over a statistically representative year, meso-macrotidal areas exhibit tide-dominance, while shallow, finer grained, amphidromic regions show wave-dominance, with WTI dominating extensively >30m depth. Seabed morphology is strongly affected by sediment transport mode, and sand wave geometry varies significantly between predicted dominance classes with increased length and asymmetry, and decreased height, for increasing wave-dominance. This approach efficiently indicates where simple non-interactive wave and tide processes may be sufficient for modelling sediment transport, and enables efficient inter-regional comparisons and sensitivity testing to changing climate conditions with applications globally.