Predictions of Wave Overtopping Using Deep Learning Neural Networks

Deep learning techniques have revolutionized the field of artificial intelligence by enabling accurate predictions of complex natural scenarios. This paper proposes a novel convolutional neural network (CNN) model that involves deep learning technologies, such as the bottleneck residual block, layer...

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Bibliographic Details
Published in:Journal of marine science and engineering 2023-10, Vol.11 (10), p.1925
Main Authors: Tsai, Yu-Ting, Tsai, Ching-Piao
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Artificial neural networks
Bayesian analysis
bottleneck residual block
Coastal structures
convolution layer
convolutional neural network
Datasets
Deep learning
deep learning technique
Hydraulics
layer normalization
Machine learning
Mathematical models
Neural networks
Optimization
Overtopping
Predictions
Probability theory
Prototypes
wave overtopping
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Summary:Deep learning techniques have revolutionized the field of artificial intelligence by enabling accurate predictions of complex natural scenarios. This paper proposes a novel convolutional neural network (CNN) model that involves deep learning technologies, such as the bottleneck residual block, layer normalization, and dropout layer, to predict wave overtopping at coastal structures under a wide range of conditions. To optimize the performance of the CNN model, the hyperparameter tuning process via Bayesian optimization is used. The results of validation demonstrate that the proposed CNN model is highly accurate in estimating wave overtopping discharge from hydraulic and structural parameters. The testing accuracy of the overtopping predictions using a prototype dataset shows that the proposed CNN model outperforms those existing machine learning models. An example application of the CNN model is presented for predicting prototype overtopping considering various crest freeboards of coastal structures.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse11101925