Predicting morphological changes along a macrotidal coastline Using a Two-Stage Machine Learning Model

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.