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dc.contributor.author王皓正en_US
dc.contributor.authorHao-Cheng Wangen_US
dc.contributor.author張憲國en_US
dc.contributor.authorHsien-Kuo Changen_US
dc.date.accessioned2014-12-12T02:24:25Z-
dc.date.available2014-12-12T02:24:25Z-
dc.date.issued2000en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#NT890015020en_US
dc.identifier.urihttp://hdl.handle.net/11536/66409-
dc.description.abstract本文是利用遺傳演算法優選短時間潮汐水位資料的調和常數後進而預測長時間後潮汐水位的變化。本文提出的方法能有效縮短成使用540小時∼720小時的實測水位資料便可長期預測潮汐水位,而優於調和分析法所需要的369天逐時水位資料才能達到不錯的預報精度。 因為潮汐水位變化除了主要受天文引潮力的影響外,仍受到地形、溫度等的影響,為了調整季節性的水位變化,本文提出一個類神經網路方法學習地理位置相鄰的測站季節特性後,作為預測某地的季節性水位變化的調整。對於有些潮差較小的測站其受到其他干擾比例較重,所以在優選時容易落入局部最佳解而導致無法預測,因此本文利用優選後的調和常數所合成的預測水位進行720小時的移動平均,並根據此水位來判別優選的調和常數是否合理。 而本文所提之預報模式若與調和分析法比較,若同樣使用11個分潮作為分析時,本文之模式僅需540小時的實測水位資料,便能達到調和分析法使用3個月的實測水位資料所預測的精度,而使用23個分潮的遺傳演算法預報模式,只需720小時的實測水位資料便能與調和分析法使用22個分潮輸入6個月的預報精度相當,因此應用遺傳演算法的潮汐預報模式比一般使用的調和分析法模式能有效縮短所需要輸入的實測潮汐水位資料。zh_TW
dc.description.abstractGenetic algorithm was applied to finding the fittest amplitude and phase lag of each tidal constituent and then to forecasting long-term tidal levels. The present model needs only 540-720 hours’ tidal data instead of a continuous tidal record for 369 days used in harmonic analysis method. The variation of astronomical tides due to topography and temperature was found by moving Gaussian average method. An artificial neural network model was proposed to deseasonalize tidal levels related to temperature. That the forecasted tidal levels after moving average of 720 hours display a variation with large period or not is suggested to be an examination for prediction accuracy. A 11-constituent model is recommended to forecast tidal levels when only tide record of 540 hours is needed to be input and has an equivalent prediction capability as the harmonic method that needs more than three-month tidal data does. The other 23-constituent model with an input of only 720-hour data for forecasting capability is compared with the harmonic analysis method with an input of 6-month data.en_US
dc.language.isozh_TWen_US
dc.subject遺傳演算法zh_TW
dc.subject基因演算法zh_TW
dc.subject類神經網路zh_TW
dc.subject潮汐預報zh_TW
dc.subjectgenetic algorithmsen_US
dc.subjectneural networken_US
dc.subjecttidalen_US
dc.title應用遺傳演算法於長期潮汐預報之研究zh_TW
dc.titleApplication of genetic algorithms to forecasting long-term tidal levelen_US
dc.typeThesisen_US
dc.contributor.department土木工程學系zh_TW
Appears in Collections:Thesis