碩士 / 國立中興大學 / 土木工程學系 / 88 / ABSTRACT
Accurate forecasting for tidal-level variation is of great importance for construction installations or human activities in maritime areas. The tidal level could be predicted conventionally by the harmonic analysis based on the least square method. Good resolution in the conventional methods demands a sufficiently long records to ascertain the parameters of the major constituents. Alternatively, while applying the harmonic equation, this paper reports an application of the artificial neural network for forecasting the long-term tide level. The present model can determine the harmonic parameters using a very short-term observed tidal records based on a learning process. Field data of three types of tides, referred as the diurnal, semidiurnal and mixed types, are used to test the performance of the present model. The results show that the major constituents can be determined only using a two-months measured data. The results also present that one-year tidal level forecasting can be satisfactorily achieved using a half-month length of observed data.
| Identifer | oai:union.ndltd.org:TW/088NCHU0015048 |
| Date | January 2000 |
| Creators | Rong-Jer Hsieh, 謝榮哲 |
| Contributors | Ching-Piao Tsai, 蔡清標 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 64 |
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