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TIME SERIES PREDICTION OF WAVE HEIGHT BY LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORK
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- SUMITANI Nagisa
- 西日本高速道路エンジニアリング関西株式会社
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- YASUDA Tomohiro
- 関西大学 環境都市工学部
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- MORI Nobuhito
- 京都大学 防災研究所
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- SHIMURA Tomoya
- 京都大学 防災研究所
Bibliographic Information
- Other Title
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- 長短期記憶ニューラルネットワークLSTMを用いた波高の時系列予測に関する研究
- チョウタンキ キオク ニューラルネットワーク LSTM オ モチイタ ナミ ダカ ノ ジケイレツ ヨソク ニ カンスル ケンキュウ
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Description
<p> The time series prediction of wave height is important for the countermeasure of high wave disaster in the coastal area. Since time series prediction of wave heights over a long recurrence period by dynamical methods is computationally expensive, statistical methods such as neural networks (NN) are considered for prediction. Among the deep learning methods, Long Short Term Memory (LSTM) is considered to be suitable for time series forecasting of wave height because it is good at handling long-term time series data. However, no research has been published so far on time series forecasting of wave height using LSTM. In this study, time series forecasting of wave height is performed using LSTM. The effects of temporal factors such as the input of explanatory variables at multiple times in the past, spatial factors such as the input range of the meteorological field, and the combination of the parameters of the LSTM were varied, and the effects of these factors on the results were compared.</p>
Journal
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- Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering)
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Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering) 77 (2), I_151-I_156, 2021
Japan Society of Civil Engineers
