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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 林國峰(Gwo-Fong Lin) | |
dc.contributor.author | Chun-Kai Chang | en |
dc.contributor.author | 張鈞凱 | zh_TW |
dc.date.accessioned | 2021-06-07T18:09:49Z | - |
dc.date.copyright | 2020-08-04 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-07-30 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16324 | - |
dc.description.abstract | 受到氣候變遷影響,預報準確度在近年來更加重要。在水庫管理方面,提前的蓄洪、洩洪的預防性減災遠優於致災後的處理,水庫操作多仰賴準確的入流量預報,現行模式僅使用觀測降雨和入流量預報未來6小時水庫入流量,但在近年因極端天氣響,需要更長時間的入流量預報,提供給決策者更充裕的調控空間。此外,現行水庫操作大多使用定率式降雨預報,而定率式預報容易給決策者確定性的假象,故近年來提倡系集雨量預報以涵蓋雨量的不確定性,以供決策者評估預報的風險。然而機率式預報在使用上無法明確給使用者指南,通常會額外選擇以簡單平均法(Ensemble Mean , EM)來整合數量眾多的系集預報結果,但簡單平均法會將極端值平滑化。 本研究提出交替預報(Switch Prediction Method, SPM)整合系集降雨產品,並與EM及各系集產品進行比較,同時提供機率式預報和定率式降雨預報,當作降雨逕流模式輸入項,預報未來72小時水庫入流量。本研究採用在水文領域上表現良好的支援向量機(Support Vector Machine, SVM)預報水庫入流量,結合多步階預報(Multi-Step Forecasting, MSF),透過反覆迭代的過程預報出未來72小時水庫入流量。 本研究以石門水庫集水區證明模式的準確性,搜集2004年至2019年共18場颱風事件的雨量資料與入流量資料。經比較各系集產品介接水庫入流量模式過後,SPM在整合系集資料方面表現良好。在SVM t+1模式方面,預報值貼近觀測值並沒有明顯峰值誤差,SVM結合MSF模式在預報未來72小時水庫入流量,其評鑑指標表現良好,可供水庫洩洪、蓄洪操作決策者參考使用。 | zh_TW |
dc.description.abstract | Affected by climate change, the forecast accuracy is more important in recent years. Reservoir operations rely on accurate inflow forecasts. Previous studies used observed rainfall and inflow to forecast reservoir inflows for 1– to 6–h lead times. However, in recent years, the long lead time forecasts are required to provide decision-makers with ample time for regulation and control due to the impact of extreme climates. Also, most of the current reservoir operations use the deterministic rainfall forecast which gives the illusion of certainty to decision-makers. Therefore, in recent years, it has promoted an ensemble rainfall forecast to cover the rainfall uncertainty for decision-makers to assess the risk of forecasts. However, the ensemble forecast cannot be used as a guide for users. The ensemble mean (EM) is conventionally used to integrate a large number of ensemble forecast results, but the EM smooths out extreme values. This study adopts a switch prediction method (SPM) to integrate ensemble rainfall products, and compares with EM and each ensemble forecast. The rainfall forecasts are then used as input to the rainfall-runoff model. The support vector machine (SVM) combined with multi-step forecasting (MSF) is used to forecast reservoir inflow for 1– to 72–h lead times through the iterative process. Rainfall and inflow data of the Shihmen Reservoir for 18 typhoons from 2004 to 2019 are employed to demonstrate the advantages of the proposed methodology. The SPM performs well in integrating the ensemble rainfall products. Regarding the SVM t+1 model, the forecasts are close to the observed and there is no obvious peak error. The SVM combined with MSF performs well in forecasting the long lead time (72 hours) inflow. | en |
dc.description.provenance | Made available in DSpace on 2021-06-07T18:09:49Z (GMT). No. of bitstreams: 1 U0001-2907202022340400.pdf: 12475409 bytes, checksum: 5620422b3b11d60f47b2989072c8af01 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 目錄 口試委員會審定書 i 誌謝 ii 中文摘要 iii Abstract iv 目錄 vi 圖目錄 ix 表目錄 xi 一、 緒論 1 1.1 研究動機與目的 1 1.2 文獻回顧 3 1.2.1 數值天氣預報 3 1.2.2 機器學習建立降雨逕流模式 4 1.3 論文架構 6 二、 研究區域與資料 7 2.1 研究區域 7 2.2 研究資料 8 2.2.1 颱風資料 8 2.2.2 系集降雨資料 11 三、 研究方法 14 3.1 交替預報 14 3.2 支援向量機 16 3.3 多步階預報 18 3.5 網格搜尋法 20 3.6 研究流程與評鑑指標 21 3.6.1 研究流程 21 3.6.2 評鑑指標 22 四、 結果與討論 24 4.1 系集降雨整合模式 24 4.1.1 參數率定結果 24 4.1.2 降雨整合結果 24 4.1.3 SPM與EM整合結果比較 32 4.2 降雨逕流模式結果 36 4.2.1參數率定結果 36 4.2.2 SVM-MSF模式驗證 39 4.2.3各模式入流量結果比較 42 4.2.4 SPM與EM介接降雨逕流模式結果比較 42 五、 結論與建議 54 5.1 結論 54 5.2 建議 56 參考文獻 57 附錄A 61 附錄B 63 附錄C 66 附錄D 68 附錄E 71 | |
dc.language.iso | zh-TW | |
dc.title | 颱風時期水庫系集流量預報之發展 | zh_TW |
dc.title | Development of ensemble reservoir inflow forecasts during typhoon periods | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 賴進松(Jihn-Sung Lai),李方中(Fang-Chung Lee),吳明璋(Ming-Chang Wu) | |
dc.subject.keyword | 水庫降雨逕流模式,交替預報,支援向量機,系集降雨預報,多步階預報, | zh_TW |
dc.subject.keyword | Reservoir rainfall-runoff model,Switch prediction method,Support vector machine,Ensemble rainfall forecast,Multi-step forecasting, | en |
dc.relation.page | 80 | |
dc.identifier.doi | 10.6342/NTU202002066 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2020-07-30 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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