蔡丁貴Tsay, Ting-Kuei臺灣大學:土木工程學研究所邱啟平Chiou, Chi-PingChi-PingChiou2010-06-302018-07-092010-06-302018-07-092009U0001-1708200911293100http://ntur.lib.ntu.edu.tw//handle/246246/187767由於基隆河上游興建員山子分洪道設施，將颱風或豪大雨造成的洪水進行部分疏導，使得原有的水位預報模式(吳等，2004)計算大華橋水位站有高估水位的現象。本研究目的，在於考量員山子分洪量等因素並修正模式，使大華橋水位能夠更精準的預報，其預報結果可提供下游洪水預警，爭取沿岸居民疏散時間。模式主要考慮分洪量、河川水位之變動與其臨前狀態之序率關係，同時加入可能影響水位變動之因素：集水區之降雨量。本文假設河川水位在某一個時刻，為其所有影響因子的線性組合，使用最小平方法，從歷史颱風或豪大雨事件之水文記錄中率定出一條具有迴歸關係特性的預報函數式，其函數式再經由模擬退火法修正。當豪大雨事件發佈時，蒐集其集水區內各站之降雨量、員山子水位等等資訊，利用此一時序性的序率遞迴關係函數式即可預報該水位站一段時間之水位變化。本模式應用柯羅莎(Krosa)、薔蜜(Jangmi)等颱風預報大華橋水位之變化，皆獲得良好之驗證。Yuansantze Flood Diversion Tunnel is located at the upstream of Keelung River. The Tunnel diverts part of flood from upstream basin discharge during typhoon periods. It results in over-estimation of forecasted water levels at the Dahua Bridge station (Wu. et al., 2004). This paper takes into accounts of flood diversion to extend capability of previous forecast model. The accurately predicted results at the Dahua Bridge Station can provide with better forecasting of downstream flood water levels. This improvement will allow inhabitants residing along river to be evacuated timely. Stochastic relation includes flood diversion, present and the antecedent records at the river gage, and rainfalls are primarily concerned in the model. This paper assumes that water stage at the outlet of watershed at any time is linear combination of all the affecting factors. Based on historical data during typhoons events, a recursive relationship is developed by employing the least squares method and simulated annealing algorithms. With the recursive relationship formula, water levels at the Dahua Bridge Station can be predicted more accurately. Present model is applied to predict water levels at the Dahua Bridge Station for the Typhoon events of Krosa and Jangmi. Good agreement between forecasted and measured results is observed.中文摘要…………………………………………………………………………… I文摘要…………………………………………………………………………… II一章 緒論 1.1前言 1.2文獻回顧 2.2.1降雨逕流或水位推估 2.2.2模擬退火演算法 2.3研究目的 3二章 模擬退火演算法之理論 5.1模擬退火法之原理及物理現象 6.2模擬退火法之搜尋方式及演算步驟 8.3模擬退火演算法影響因子之探討 12三章 模式建立 15.1序率分析法 15.2最小二乘法 17.3模擬退火演算法於集水區參數調整之運用 18.4建置步驟 21四章 模式驗證 25.1研究區域 25.2集水區出水口大華橋之水位預報模式驗證 28.2.1模式率定 29.2.2模式驗證 32.3淡水河全流域不恆定流河川水位預報模式驗證 36.4員山子分洪流量 43.4.1員山子分洪工程概述 43.4.2馬斯金更法(Muskingum method) 48五章 結論與建議 54.1結論 54.2建議 56考文獻 57錄一 全流域不恆定流模式介紹 60.1 模式理論 60.1.1控制方程式 60.1.2第二種多方式特徵法 62.2 邊界條件 63.3 全流域不恆定流模式架構 63.4 模組限制及注意事項 68.5 模組應用 721922834 bytesapplication/pdfen-US序率最小平方法模擬退火法水位預報Water level forecastStochasticLeast squares methodSimulated annealing algorithmsthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/187767/1/ntu-98-R96521326-1.pdf