ONE DAY AHEAD WAVE PREDICTIONS USING A HYBRID ALGORITHM OF LONG-SHORT TERM MEMORY AND NEURAL NETWORK FOR MARINE CONSTRUCTIONS

Sooyoul Kim, Masahide Takeda, Chisato Hara, Hajime Mase
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Abstract

Recently, marine construction work has increased under complex and strict conditions for large-scale marine sites and facilities. Accurate wave information at the work site is critical to conducting the marine construction safely. In particular, making decisions for the execution of the operation need a significant wave height ranging from 0.5 to 1.0 m as a threshold. However, studies on highly accurate real-time wave height predictions around the threshold are few. The present study developed a hybrid algorithm for real-time wave prediction by combining long-short term memory (LSTM) and artificial neural network (ANN) for the Hitachinaka Port, Japan.
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基于长短期记忆与神经网络的海洋建筑前一日海浪预测
近年来,大型海洋场地和设施在复杂和严格的条件下,海洋建设工作不断增加。准确的现场海浪信息对海上施工的安全进行至关重要。特别是,在做出执行操作的决策时,需要0.5到1.0 m的显著波高作为阈值。然而,在阈值附近进行高精度实时波高预测的研究很少。本研究针对日本日中港开发了一种结合长短期记忆(LSTM)和人工神经网络(ANN)的实时波浪预测混合算法。
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