碩士 / 國立交通大學 / 土木工程系 / 88 / It is a good way to use artificial neural network (ANN) to predict hourly tidal level, especially when tidal of only few days are available to be input. However, a simple ANN model still has a drawback that the simple ANN model are poor to predict high and low tidal level when short-time neap tidal data are input. Therefore, we propose a complemental input of tidal envelope obtained from 2D or 3D tidal theory into the simple ANN model to promote its prediction ability. Five sets of tidal data of three kinds of different tidal type were collected from five stations. Comparisons of predicted peak error and mean error of three models show that the ANN model combined with 3D tidal theory is the best to predict tidal levels for all chosen data and that the simple ANN model is the poorest model. When hourly neap tidal data of one day of Penghu station are input in three models, hybrid ANN model with 3D tidal theory has smaller mean error than the simple ANN model by 20% in general and reduces peak error by 40% from the simple ANN model. It also proven that the ANN with 3D tidal theory is prior to harmonic analysis method to predict tidal levels when fifteen-day data were used.
| Identifer | oai:union.ndltd.org:TW/088NCTU0015011 |
| Date | January 2000 |
| Creators | Shu-Hsia Ma, 馬樹俠 |
| Contributors | Hsien-Kuo Chang, 張憲國 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 134 |
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