DEVELOPMENT OF A DEEP-LEARNING BASED WAVE FORECASTING MODEL USING LSTM NETWORK
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How to Cite

Kyaw, T. O., Shibayama, T., Shibutani, Y., & Kotake, Y. (2020). DEVELOPMENT OF A DEEP-LEARNING BASED WAVE FORECASTING MODEL USING LSTM NETWORK. Coastal Engineering Proceedings, (36v), waves.31. https://doi.org/10.9753/icce.v36v.waves.31

Abstract

Forecasting of wave conditions plays an essential role for offshore construction and maintenance. Recently, machine learning-based wave forecasting models have been developed and their integrated usage with physics-based numerical models has become popular. These studies mostly apply Feed Forward Neural Networks (FFNNs) with an emphasis on prediction of time-series of waves, tides and storm surges. As a particularly different approach, we develop a deep learning-based wave forecasting model using Long Short-Term Memory (LSTM) network under Recurrent Neural Networks. As a case study, the model will be utilized to predict the wave conditions (low or high) near the Tottori Port, Japan.

Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/oMvIS9zkIOs
https://doi.org/10.9753/icce.v36v.waves.31
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References

O’Donncha, Zhang, Chen, and James (2018): An integrated framework that combines machine learning and numerical models to improve wave-condition forecasts, Journal of Marine Systems, 186, pp. 29-36.

Kim, Matsumi, Pan, and Mase (2016): A real-time forecast model using artificial neural network for after-runner storm surges on the Tottori Coast, Japan, Ocean Engineering,122, pp. 44-53.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.