Predicting Morphological Changes Along a Macrotidal Coastline Using a Two‐Stage Machine Learning Model
Abstract 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 (2007–202...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-04-01
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| Series: | Water Resources Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024WR037523 |
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| Summary: | Abstract 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 (2007–2022). Wave data at these 125 locations were simulated using the hydrodynamic Delft3D model, with full coupling of the Delft3D FLOW and WAVE modules. 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 change in sediment volume after erosion/accretion. The random forest classifier achieves testing accuracy of 0.74. LSTM model achieved a testing regression of 0.92 for one‐step‐ahead (6 months) predictions of change in sediment volume time series, while sequence‐to‐sequence model achieved the testing regression of 0.96 for three‐time‐ahead (1.5 years) predictions and 0.88 for ten‐time‐step‐ahead (5 years) prediction. |
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| ISSN: | 0043-1397 1944-7973 |