AbstractForecasting 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
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.
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