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.