Please use this identifier to cite or link to this item:
標題: 倒傳遞類神經網路在波浪預報之應用
Back-Propagation Neural Network on Wave Forecast
作者: 朱良瀚
chu, liang-han
關鍵字: Box-Jenkins;倒傳遞類神經網路;neural network;wave forecast;波浪即時預報;時序列分析模式
出版社: 土木工程學系

In this paper, the back-propagation neural network (BPN)
associated the I/O relationship in the Box-Jenkins model
isestablished for the wave forecast model. The virtue of
artificialneural network model is available for the short-term
time series, thus it is useful for the wave prediction of
offshore and coastal regions. A time series of Bretschneider
wave spectrum performed in the laboratory is firstly adopted in
this study to optimize the algorithm and network topology of the
back-propagation neural network. The site wave data measured at
Taichung Harbor and Kaoshiung LNG Port are then used to verify
the accuracy of the model, based on the analysis of the
efficiency coefficient, correlation coefficient and the root
mean squared error between predicted and observed data. Waves of
winter type and summer type are respectively simulated in the
verification of model. The results show that the prediction has
good performance in the winter waves when the short or longer
training data is used. However, a longer training data should be
utilized to have better performance for the summer waves due to
storm waves being involved in the season.
Appears in Collections:土木工程學系所

Show full item record

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.