Abstract
Significant Wave Height is an ocean wave characteristic that plays a major role in deriving and predicting wave energy. In this study, we aim to predict significant wave height using wind data as input to a multi-layer perceptron (MLP) neural network and analyze the developed network under several scenarios. Accordingly, the network was tested using different learning rates and numbers of layers. The computational times and results for all of the iterations and training periods were also recorded. Also, the acquired results of the models with better performances were compared with real data acquired from buoys. The results imply that all of the applied MLP networks could learn the relationships between wind and wave height and predict them. These MLP neural networks are composed of many operations that are either element-wise, or are represented as matrix-multiplications, making them good candidates for hardware (GPU) acceleration. This work illustrates the efficacy of wave height forecasting using MLP networks with GPU acceleration.
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Acknowledgements
This material is based in part upon work supported by the National Science Foundation under grant number(s) DUE-2142360, OIA-2019609, and OIA-2148788. Any opinions, findings conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, the Institute of Education Sciences, or the U.S. Department of Education.
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Reisi-Dehkordi, A., Reeves, S.I., Harris, F.C. (2024). GPU-Accelerated Neural Networks and Computational Strategies to Predict Wave Heights. In: Latifi, S. (eds) ITNG 2024: 21st International Conference on Information Technology-New Generations. ITNG 2024. Advances in Intelligent Systems and Computing, vol 1456. Springer, Cham. https://doi.org/10.1007/978-3-031-56599-1_47
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