Artificial Intelligence Application on Sediment Transport

When erosion occurs, sand beaches cannot maintain sufficient sand width, foreshore slopes become steeper due to frequent erosion effects, and beaches are trapped in a vicious cycle of vulnerability due to incident waves. Accordingly, beach nourishment can be used as a countermeasure to simultaneousl...

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Bibliographic Details
Published inJournal of marine science and engineering Vol. 9; no. 6; p. 600
Main Authors Kim, Hyun Dong, Aoki, Shin-ichi
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2021
Subjects
Artificial intelligence
Artificial neural networks
Beach erosion
Beach nourishment
Beach slope
Beaches
Coasts
Computer simulation
deep learning
Environmental impact
Experiments
Foreshore
Gravel
Hydraulic models
Hydraulics
Incident waves
Mathematical models
Neural networks
Numerical methods
Numerical models
Sand
Sand & gravel
Sand transport
Sediment
Sediment transport
Shoreline protection
Soil erosion
Training
Vulnerability
Wave power
Japan
Tuvalu
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Summary:When erosion occurs, sand beaches cannot maintain sufficient sand width, foreshore slopes become steeper due to frequent erosion effects, and beaches are trapped in a vicious cycle of vulnerability due to incident waves. Accordingly, beach nourishment can be used as a countermeasure to simultaneously minimize environmental impacts. However, beach nourishment is not a permanent solution and requires periodic renourishment after several years. To address this problem, minimizing the period of renourishment is an economical alternative. In the present study, using the Tuvaluan coast with its cross-sectional gravel nourishment site, four different test cases were selected for the hydraulic model experiment aimed at discovering an effective nourishment strategy to determine effective alternative methods. Numerical simulations were performed to reproduce gravel nourishment; however, none of these models simultaneously simulated the sediment transport of gravel and sand. Thus, an artificial neural network, a deep learning model, was developed using hydraulic model experiments as training datasets to analyze the possibility of simultaneously accomplishing the sediment transport of sand and gravel and supplement the shortcomings of the numerical models.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse9060600