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Indexed by:期刊论文

Date of Publication:2008-05-01

Journal:OCEAN ENGINEERING

Included Journals:SCIE、EI

Volume:35

Issue:7

Page Number:666-675

ISSN No.:0029-8018

Key Words:neural network; tidal level; prediction; supplement; non-astronomical components

Abstract:Accurate prediction of tidal level including strong meteorologic effects is very important for human activities in oceanic and coastal areas. The contribution of non-astronomical components to tidal level may be as significant as that of astronomical components under the weather, such as typhoon and storm surge. The traditional harmonic analysis method and other models based on the analysis of astronomical components do not work well in these situations. This paper describes the Back-Propagation Neural Network (BPNN) approach, and proposes a method of iterative multi-step prediction and the concept of periodical analysis. The prediction among stations shows that the BPNN model can predict the tidal level with great precision regardless of different tide types in different regions. Based on the non-stationary characteristic of hourly tidal record including strong meteorologic effects, three Back-Propagation Neural Network models were developed in order to improve the accuracy of prediction and supplement of tidal records: (1) Difference Neural Network model (DNN) for the supplementing of tidal record; (2) Minus-Mean-Value Neural Network model (MMVNN) for the corresponding prediction between tidal gauge stations; (3) Weather-Data-based Neural Networks model (WDNN) for set up and set down.
   The results show that the above models perform well in the prediction of tidal level or supplement of tidal record including strong meteorologic effects. (C) 2008 Elsevier Ltd. All rights reserved.