臺灣博碩士論文加值系統

本論文研究南北通橋集水區颱風逕流洪峰流量及發生時間之預測，首先應用基因演算法優選單位歷線以率定颱風降雨與逕流之關係。然後於接續之預測驗證時，有效雨量以模糊Φ值間接估算，可相當精準地預測逕流洪峰及發生時間。
研究案例選用中央氣象局對於颱風路徑之第1、2、3、6、9等五類路徑颱風，原則上每一路徑自中央氣象局網站蒐集兩場颱風，其中一場颱風率定降雨及逕流關係，另外一場颱風用於模式驗證(但第1、9路徑無驗證案例)。案例南北通橋集水區位於台灣中部烏溪流域支流北港溪，該集水區有清流、惠蓀與翠巒等三處雨量站及南北通橋流量站，颱風期間之降雨與逕流觀測資料蒐集自經濟部水利署。
本研究對於率定案例之預測結果，每一路徑颱風案例之預測率定結果都甚佳，其中第1類路徑之尼爾遜颱風洪峰流量預測誤差+0cms (誤差率+0%)、第2類路徑之揚希颱風洪峰流量預測誤差+0cms (誤差率+0%)、第3類路徑之莎拉颱風洪峰流量預測誤差+1cms (誤差率+0.26%)、第6類路徑之亞力士颱風洪峰流量預測誤差-2cms (誤差率-0.58%)、第9類路徑之韋恩颱風洪峰流量預測誤差+1cms (誤差率+0.61%)。至於在洪峰流量發生時間之預測結果，除第3類路徑之莎拉颱風晚了2小時以外，其餘各路徑之時間預測誤差皆為0小時，亦即完全符合。
本研究對於驗證案例之預測結果，以第2類颱風路徑與第6類颱風路徑之預測結果最好，其中賀伯颱風洪峰流量預測誤差+8cms (誤差率+1.63%)、道格颱風洪峰流量預測誤差-2cms (誤差率-0.27%)。第3類颱風路徑預測結果不佳，桃芝颱風洪峰流量預測誤差-641cms (誤差率-41.90%)。另外在洪峰流量發生時間之預測結果，第2類路徑之賀伯颱風晚了1小時、第6類路徑之道格颱風晚了3小時、第3類路徑之桃芝颱風晚了1小時。
驗證結果較差的部份是桃芝颱風洪峰流量預測誤差-641cms，其原因是桃芝颱風的雨型相當特別，瞬間降下大量暴雨，隨之產生巨大洪峰流量，所以驗證桃芝颱風才會失準。所以未來若有第3類路徑颱風在案例集水區發生時，建議增加桃芝颱風之案例，與莎拉颱風並列為兩個參考前例，同時據以進行預測，再對照集水區當時之實際逕流情況，研判其特性是趨向莎拉颱風或桃芝颱風。為了便於應用，本研究亦優選出桃芝颱風之單位歷線，其率定之洪峰流量預測誤差-1cms（誤差率-0.07%）、發生時間預測誤差晚了1小時。

Prediction on magnitude and reaching time of peak flow for typhoon-induced floods were undertaken for Nan-Bei-Tong Bridge watershed in this paper. The optimal unit hydrograph is obtained by using genetic algorithms to calibrate rainfall-runoff relationship. The fuzzy Φvalue is added for the purpose of precise prediction.
No.1, No.2, No.3, No.6, and No.9 tracks, classified by CWB, were selected for case study. A pair of typhoon cases from each track, one for calibration, the other for validation except No.1 and No.9 tracks, that is, 8 typhoon events were studied in this paper. The study watershed is located at sub-stream called Bei-Gang creek within Wu-Si river basin in Central Taiwan. There are three rainfall stations named Ching-Liu, Huei-Sun, and Cuei-Fong, and one stage station named Nan-Bei-Tong Bridge in this watershed. The typhoons information is downloaded from website of Central Weather Bureau, and the observed data of rainfall and runoff are collected from Water Resources Agency.
For the calibration procedures, the errors limits less than 1% the deviations of magnitude of peal flow is +0cms (error ratio +0%) of Typhoon Nelson (No.1 track), +0cms (error ratio +0%) of Typhoon Yancy (No.2 track), +1cms (error ratio +0.26%) of Typhoon Sarah (No.3 track), -2cms (error ratio -0.58%) of Typhoon Alex (No.6 track), and +1cms (error ratio +0.61%) of Typhoon Wayne (No.9 track); time of peak flow is perfectly fitted except Typhoon Sarah is 2 hours late.
For the prediction procedures, No.2 and No.6 tracks own high credits, while No.3 track is poor. The deviation of magnitude are +8cms (error ratio +1.63%) of Typhoon Herb (No.2 track), -641cms (error ratio -41.90%) of Typhoon Toraji (No.3 track), and -2cms (error ratio -0.27%) of Typhoon Doug (No.6 track), respectively; the reaching time of peak flow are 1 hour late for Typhoon Herb, 1 hour late for Typhoon Toraji, and 3 hours late for Typhoon Doug.
The dominant reason for Typhoon Toraji was not well predicted was investigated. The author believed that the rainfall pattern of Typhoon Toraji is unique and not compatible. Prediction of No.3 track typhoon-induced flood in this watershed is then recommended to use both of two typical past samples, Typhoon Toraji and Typhoon Sarah.

中文摘要………………………………………………………Ⅰ
英文摘要………………………………………………………Ⅳ
章節目錄…………………………………………………………Ⅶ
圖次目錄…………………………………………………………Ⅹ
表次目錄…………………………………………………………ⅩⅢ
符號說明………………………………………………………ⅩⅣ

第一章 前言……………………………………………………1
第一節 研究動機…………………………………………1
第二節 研究目的…………………………………………2
第三節 研究流程…………………………………………3
第二章 前人研究……………………………………………………5
第一節 洪水流量預測……………………………………5
第二節 模糊邏輯…………………………………………8
第三節 基因演算法………………………………………9
第三章 研究材料與案例…………………………………………11
第一節 案例集水區…………………………………………11
第二節 案例颱風之選取…………………………………13
第三節 降雨及逕流觀測資料……………………………24
第四章 預測模式之率定…………………………………………30
第一節 Φ入滲指數之估算………………………………30
第二節 單位歷線之優選……………………………………32
第三節 率定結果討論………………………………………56
第五章 預測模式之驗證………………………………………60
第一節 模糊Φ值之推估……………………………………60
第二節 洪峰流量之預測……………………………………74
第三節 預測結果討論………………………………………77
第六章 綜合討論………………………………………………81
第一節 颱風路徑之影響……………………………………81
第二節 單位歷線之適用……………………………………82
第三節 基因演算之應用……………………………………83
第四節 模糊邏輯之應用……………………………………84
第五節 中間流之估算………………………………………84
第六節 未來之即時預測……………………………………88
第七節 其他預測輔助指標…………………………………91
第七章 結論與建議……………………………………………93
第一節 結論…………………………………………………93
第二節 建議…………………………………………………95
參考文獻 …………………………………………………………97
附錄一 案例颱風之動態及災情描述………………………104
附錄二 案例颱風之侵台路徑圖……………………………110
附錄三 案例颱風之總雨量分布圖…………………………114
附錄四 案例颱風之時雨量分布圖…………………………117
附錄五 案例颱風之降雨及逕流紀錄………………………123
附錄六 降雨及逕流雙累積曲線圖…………………………147

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