Shoreline predictive model using artificial intelligence for the homogeneous beach of the Western Coast of Ghana
Coastal areas are preferred home to about 40% of global population than any other ecosystems. Many organisms, including endangered species, live along sandy beaches. Nonetheless, such beaches are prone to erosion and flooding owing to natural and human factors. The shoreline, where land meets the s...
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| Format: | Article |
| Language: | English |
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Society of Land Measurements and Cadastre from Transylvania (SMTCT)
2025-03-01
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| Series: | Nova Geodesia |
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| Online Access: | https://novageodesia.ro/index.php/ng/article/view/303 |
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| author | Cynthia Borkai BOYE Peter Ekow BAFFOE Paul BOYE |
| author_facet | Cynthia Borkai BOYE Peter Ekow BAFFOE Paul BOYE |
| author_sort | Cynthia Borkai BOYE |
| collection | DOAJ |
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Coastal areas are preferred home to about 40% of global population than any other ecosystems. Many organisms, including endangered species, live along sandy beaches. Nonetheless, such beaches are prone to erosion and flooding owing to natural and human factors. The shoreline, where land meets the sea, is highly dynamic and challenging to predict with accuracy. Existing predictive numerical models rely on multiple parameters, while statistical analysis of historical shoreline positions assume a linear distribution, overlooking the nonlinear nature of the data. This present study explored the application of nonlinear approaches like artificial neural networks (ANN) and other artificial intelligence techniques to predict shoreline change rate along the sandy beach of the study area using recurrent neural networks approaches: nonlinear autoregressive neural network (NARNN) and nonlinear autoregressive exogenous neural network (NARXNN); backpropagation neural network (BPNN), and compared results with multiple linear regression (MLR) model. Data used was partitioned into two: 70% was used for training and 30% was reserved for evaluating the performance of the models. From the developed models the output forecast for shoreline change were determined. NARXNN yielded best prediction, followed closely by NARNN and then BPNN as against MLR model. The optimum model developed for shoreline prediction provides invaluable information for planners and engineers of the coast.
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| format | Article |
| id | doaj-art-9b04bf84019d474ab3d2d8c3981d5e0c |
| institution | DOAJ |
| issn | 2810-2754 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Society of Land Measurements and Cadastre from Transylvania (SMTCT) |
| record_format | Article |
| series | Nova Geodesia |
| spelling | doaj-art-9b04bf84019d474ab3d2d8c3981d5e0c2025-08-20T02:50:00ZengSociety of Land Measurements and Cadastre from Transylvania (SMTCT)Nova Geodesia2810-27542025-03-015110.55779/ng51303Shoreline predictive model using artificial intelligence for the homogeneous beach of the Western Coast of GhanaCynthia Borkai BOYE0Peter Ekow BAFFOE1Paul BOYE2University of Mines and Technology, TarkwaUniversity of Mines and Technology, TarkwaUniversity of Mines and Technology, Tarkwa Coastal areas are preferred home to about 40% of global population than any other ecosystems. Many organisms, including endangered species, live along sandy beaches. Nonetheless, such beaches are prone to erosion and flooding owing to natural and human factors. The shoreline, where land meets the sea, is highly dynamic and challenging to predict with accuracy. Existing predictive numerical models rely on multiple parameters, while statistical analysis of historical shoreline positions assume a linear distribution, overlooking the nonlinear nature of the data. This present study explored the application of nonlinear approaches like artificial neural networks (ANN) and other artificial intelligence techniques to predict shoreline change rate along the sandy beach of the study area using recurrent neural networks approaches: nonlinear autoregressive neural network (NARNN) and nonlinear autoregressive exogenous neural network (NARXNN); backpropagation neural network (BPNN), and compared results with multiple linear regression (MLR) model. Data used was partitioned into two: 70% was used for training and 30% was reserved for evaluating the performance of the models. From the developed models the output forecast for shoreline change were determined. NARXNN yielded best prediction, followed closely by NARNN and then BPNN as against MLR model. The optimum model developed for shoreline prediction provides invaluable information for planners and engineers of the coast. https://novageodesia.ro/index.php/ng/article/view/303nonlinear autoregressive neural network with external inputsandy beachesshoreline dynamics prediction |
| spellingShingle | Cynthia Borkai BOYE Peter Ekow BAFFOE Paul BOYE Shoreline predictive model using artificial intelligence for the homogeneous beach of the Western Coast of Ghana Nova Geodesia nonlinear autoregressive neural network with external input sandy beaches shoreline dynamics prediction |
| title | Shoreline predictive model using artificial intelligence for the homogeneous beach of the Western Coast of Ghana |
| title_full | Shoreline predictive model using artificial intelligence for the homogeneous beach of the Western Coast of Ghana |
| title_fullStr | Shoreline predictive model using artificial intelligence for the homogeneous beach of the Western Coast of Ghana |
| title_full_unstemmed | Shoreline predictive model using artificial intelligence for the homogeneous beach of the Western Coast of Ghana |
| title_short | Shoreline predictive model using artificial intelligence for the homogeneous beach of the Western Coast of Ghana |
| title_sort | shoreline predictive model using artificial intelligence for the homogeneous beach of the western coast of ghana |
| topic | nonlinear autoregressive neural network with external input sandy beaches shoreline dynamics prediction |
| url | https://novageodesia.ro/index.php/ng/article/view/303 |
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