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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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR037523 |
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| author | Pavitra Kumar Nicoletta Leonardi |
| author_facet | Pavitra Kumar Nicoletta Leonardi |
| author_sort | Pavitra Kumar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8dee63598e59480ebb7cdcead3e7bc79 |
| institution | DOAJ |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-8dee63598e59480ebb7cdcead3e7bc792025-08-20T03:22:16ZengWileyWater Resources Research0043-13971944-79732025-04-01614n/an/a10.1029/2024WR037523Predicting Morphological Changes Along a Macrotidal Coastline Using a Two‐Stage Machine Learning ModelPavitra Kumar0Nicoletta Leonardi1Department of Geography and Planning School of Environmental Sciences University of Liverpool Liverpool UKDepartment of Geography and Planning School of Environmental Sciences University of Liverpool Liverpool UKAbstract 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.https://doi.org/10.1029/2024WR037523sediment volumemorecambe baytwo‐stage modelingrandom forestLSTMsequence‐to‐sequence |
| spellingShingle | Pavitra Kumar Nicoletta Leonardi Predicting Morphological Changes Along a Macrotidal Coastline Using a Two‐Stage Machine Learning Model Water Resources Research sediment volume morecambe bay two‐stage modeling random forest LSTM sequence‐to‐sequence |
| title | Predicting Morphological Changes Along a Macrotidal Coastline Using a Two‐Stage Machine Learning Model |
| title_full | Predicting Morphological Changes Along a Macrotidal Coastline Using a Two‐Stage Machine Learning Model |
| title_fullStr | Predicting Morphological Changes Along a Macrotidal Coastline Using a Two‐Stage Machine Learning Model |
| title_full_unstemmed | Predicting Morphological Changes Along a Macrotidal Coastline Using a Two‐Stage Machine Learning Model |
| title_short | Predicting Morphological Changes Along a Macrotidal Coastline Using a Two‐Stage Machine Learning Model |
| title_sort | predicting morphological changes along a macrotidal coastline using a two stage machine learning model |
| topic | sediment volume morecambe bay two‐stage modeling random forest LSTM sequence‐to‐sequence |
| url | https://doi.org/10.1029/2024WR037523 |
| work_keys_str_mv | AT pavitrakumar predictingmorphologicalchangesalongamacrotidalcoastlineusingatwostagemachinelearningmodel AT nicolettaleonardi predictingmorphologicalchangesalongamacrotidalcoastlineusingatwostagemachinelearningmodel |