請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/600
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 康仕仲(Shih-Chung Kang) | |
dc.contributor.author | Yi-Lin Chan | en |
dc.contributor.author | 詹益淋 | zh_TW |
dc.date.accessioned | 2021-05-11T04:40:25Z | - |
dc.date.available | 2020-08-20 | |
dc.date.available | 2021-05-11T04:40:25Z | - |
dc.date.copyright | 2019-08-20 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-20 | |
dc.identifier.citation | Bauer, M., & Johnson-Laird, P. 1993. How diagrams can improve reasoning. Psychological Science, 4: 372-378.
Bertin, J. 1981. Graphics and graphic information-processing. Berlin, Germany: Walter de Gruyter. Burkhard, R. A. 2004. Learning from architects: The difference between knowledge visualization and information visualization. Paper presented at the 8th International Conference on Information Visualisation, London, UK. Crapo, A. W., Waisel, L. B., Wallace, W. A., & Willemain, T. R. 2000. Visualization and the process of modeling: A cognitive-theoretic view. Paper presented at the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA. Ebert, Elizabeth E., 2001: Ability of a Poor Man's Ensemble to Predict the Probability and Distribution. Mon. Wea. Rev., 129, 2461–2480. Fang, X., and Y.-H. Kuo, 2013: Improving ensemble-based quantitative precipitation forecasts for topography-enhanced typhoon heavy rainfall over Taiwan with a modified probability-matching technique. Mon. Wea. Rev., 141, 3908–3932. Finley, J.P., 1884: Tornado predictions. Amer. Meteor. J., 1, 85 -88. Glenberg, A., & Langston, M. 1992. Comprehension of illustrated text: Pictures help to build mental models. Journal of Memory and Language, 31: 129-151. Kamal, N., Smith, E. E., Stephenson, C., Choi, P. M., Goyal, M., & Hill, M. D. 2015. Visualizing acute stroke data to improve clinical outcomes. Stroke, 46(7), e170-e172. Larkin, J. & Simon, H. 1987. Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11: 65-100. Rhyne, T., & Chen, M. 2013. Cutting-edge research in visualization. IEEE Computer, 46(5): 22-24. Robber P.-J., 2009: Notes and Correspondence Visualizing Multiple Measures of Forecast Quality. Wea. Forecasting, 24, 601 -608. Shneiderman, B. 2010. Designing the user interface: Strategies for effective: Human computer interaction (5th Ed.). Upper Saddle River, NJ: Pearson. Stolte, C., Tang, D., & Hanrahan, P. 2002. Polaris: A system for query, analysis, and visualization of multidimensional relational databases. Visualization and Computer Graphics, 8(1): 52-65. Tidwell, Ware, C. 2004. Information visualization: Perception for design (2nd Ed.). San Francisco, CA: Morgan Kaufman. 郭鴻基、陳郁涵、蘇世顥、徐理寰、楊憶婷,2013,臺灣颱風雨不停,農業世界雜誌,359期:頁42-53。 蔡孟涵、黃詩閔、康仕仲、賴進松,2013,防災決策支援系統,災害防救科技與管理學刊,2卷2期:頁21-33。 康仕仲、李清勝、何興亞,2009,防災決策支援系統更新及配套措施作業流程規劃之研究(1/2),經濟部水利署期末報告(MOEAWRA0980080)。臺北市:經濟部水利署 康仕仲、李清勝、何興亞,2009,防災決策支援系統更新及配套措施作業流程規劃之研究(2/2),經濟部水利署期末報告(MOEAWRA0990039)。臺北市:經濟部水利署 康仕仲、李清勝、何興亞,2009,防災決策支援系統更新及配套措施作業流程規劃之研究(2/2),經濟部水利署期末報告(MOEAWRA0990039)。臺北市:經濟部水利署 黃椿喜、葉世瑄、呂國臣、洪景山,2013,氣象局官方與主要數值天氣預報指引之定量降水預報校驗與綜合比較,104年天氣分析與預報研討會論文彙編,A7-11。 李志昕、洪景山,2014,區域系集定量降水預報之應用與分析研究,103年天氣分析與預報研討會論文摘要彙編,A2-19。 葉世瑄、林沛練、洪景山、黃椿喜,2014,機率擬合之系集定量降水預報後處理方法,103年天氣分析與預報研討會論文摘要彙編,A6-6。 葉世瑄,2014,系集定量降水預報方法之研究。國立中央大學大氣物理研究所碩士論文。 江宙君、陳嬿竹、徐理寰、吳明璋、黃麗蓉、林忠義,2015,定量降雨系集預報加值雨量測試分析。104年天氣分析與預報研討會,A2-65。 蘇奕叡、李志昕、洪景山,2015,系集機率擬合定量降水預報產品之特性分析,104年天氣分析與預報研討會,A2-66。 吳明璋、王潔如、徐理寰、張龍耀、陳嬿竹、蕭玲鳳、洪景山、李清勝,2016,系集定量降水預報群集分析技術之發展與評估,105年天氣分析與預報研討會,A2-11。 王潔如、吳明璋、徐理寰、蕭玲鳳、洪景山、李清勝,2017,系集定量降水群集分析技術之測試與應用,105年天氣分析與預報研討會,A2-33。 于宜強、李宗融,2017,颱洪災害應變的情資研判服務,災害防救科技與管理學刊,6 卷1 期:頁63-78。 吳東昇、王藝峰,2009,臺灣雨量警戒值淹水預警系統之研究,2009臺灣災害管理研討會,臺北市:社團法人臺灣災害管理學會。 謝明昌、康仕仲、耿承孝、陳奕竹、莊世坤、蔡孟涵,2016,運用雨量預報提前掌握可能淹水資訊,105年天氣分析與預報研討會,A4-25。 郭純伶、賴進松、楊介良、張成璞、詹益淋、張向寬,2018,應用降雨預報資訊進行防汛熱點評估,107年天氣分析與預報研討會,A5-25。 呂偉僑、吳淑娟、蘇希洵、張敬仁、孫天龍、黃建華,2015,以互動式可視化儀表盤做為社區老人跌倒風險評估工具,台灣復健醫學雜誌,643卷2期:頁99-110。 顏宛青(2005),動態視覺化融入國小二年級數學學習成效之研究~以乘法、等分除、重量和體積、查月曆為例,國立臺南大學教育經營與管理研究所數學科教學碩士班碩士論文。 宋爾軒、蔡孟涵、康仕仲、賴進松、譚義績,2014,防災資訊儀表板開發研究,災害防救科技與管理學刊,3卷1期:頁69-94。 朱容練、李正國、劉俊志、林士堯、詹景良、楊鈞宏、朱吟晨、陳永明、林李耀,2015,氣象水文資料於旱象與水資源監測預警資訊之應用,土木水利,42卷5期:頁33-38 經濟部水利署,2018,107水利防災年報,臺北市:經濟部水利署 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/handle/123456789/600 | - |
dc.description.abstract | 為了讓防災情資保有氣象預報的不確定性,本研究提出了系集決策法以輔助防災決策作業。系集決策法包含三個步驟,依序是建構防災情資數據模型,開發系集預警圖資,以及建立決策流程。在第一個步驟中,本研究直接使用氣象預報中各個系集成員的預報資料、降雨淹水門檻值以及防汛熱點進行災害模擬運算。在第二個步驟內,本研究開發基於資料視覺化的系集預警圖資,利用三種預警圖資來呈現災害模擬結果,包含縣市淹水預警威脅圖、鄉鎮淹水預警時序圖與多維淹水預警陣列圖。第三個步驟是建立決策流程,讓決策者可依循這個流程,依序進行威脅判斷、啟動評估以及確認應變強度三項作業。
為了驗證方法的有效性,本研究以2019年0517豪雨、0518豪雨與0520豪雨3場事件進行實驗,在臺灣設立淹水預警門檻值的363個鄉鎮市區裡面,3場事件的實際淹水數量分別為15個、1個與55個。驗證時以現有決定性預報及系集決策法達各門檻數量的一級淹水預警、及系集決策法達各門檻數量的二級淹水預警等三類方法進行比較。在淹水災情比較部份,利用淹水災情發生前24小時內的三次預報結果與實際災情做比較。決定性預報在0517豪雨與0518豪雨2場事件中無預警能力,在0520豪雨事件中的預兆得分分別是0.03、0.07與0.05。系集決策法在0517豪雨事件下的最佳預兆得分分別是0.13、0.44與0.71,其中051620初始場內的達4個二級淹水預警方法,其預兆得分是0.71,表示預警能力為71%,偏離指數是0.93,表示其接近無偏的預警,是本次研究統計資料內的最佳預警;在0518豪雨事件中各方法皆無預警能力;在0520豪雨事件中的最佳預兆得分分別是0.31、0.25與0.36。 在0517豪雨事件中,因降雨條件穩定,系集決策法隨預警時間逐漸接近實際淹水災情發生時間,對淹水災情的掌握度越來越好;在0518豪雨事件中,發生淹水災情的鄉鎮市區為1個,決定性預報與系集決策法對淹水災情的掌握度皆不足;在0520豪雨事件中,因降雨條件不穩定,加上淹水警戒無法充分表現實際發生淹水災情的鄉鎮市區,因此決定性預報與系集決策法下的各方法對災情掌握度皆不足。經過2019年的3場豪雨事件檢驗後,足可證明系集決策法產生的淹水預警較決定性預報所產生的淹水預警更能掌握實際淹水災情。 | zh_TW |
dc.description.abstract | This research proposes the Ensemble Decision Method to assist disaster prevention decision-making and to make the disaster prevention information keep the uncertainty of meteorological forecast. The Ensemble Decision Method included three steps: (1) build a disaster prevention data model to perform the disaster simulation operation; (2) develop an ensemble pre-alert map to visualize the ensemble alert map; and (3) establish a decision process to allow decision makers to conduct threat assessments, initiate assessments and confirm strain strength.
In order to verify the effectiveness of the method, this research carried out experiments with 0517 heavy rains, 0518 heavy rains and 0520 heavy rains in 2019. In the 363 townships where the rainfall threshold was set up in Taiwan. The actual flood disaster amount of the three events was respectively 15, 1 and 55. In the experiment, we used existing decisive forecast, various ensemble decision method for reaching the first-level flood pre-alert, and various ensemble decision method for reaching the second-level flood pre-alert. The results of the three predictions within 24 hours before the flooding occurred were compared with the actual disaster situation. The decisive forecast had no pre-alert ability in the 0517 heavy rain and 0518 heavy rain events, and in the 0520 heavy rain event, The threat score of decisive forecast was respectively 0.03, 0.07, and 0.05. In the 0517 heavy rain event, the best threat score of the ensemble decision method was respectively 0.13, 0.44 and 0.71. In the initial field of 051620, The threat score of the method which four modes reached the second-level flood pre-alert of the ensemble decision method was 0.71, indicating the pre-alert capability was 71%. The bias score was 0.93, indicating that it was close to unbiased pre-alert. This was the best pre-alert data in this research. The ensemble decision method had no pre-alert ability in the 0518 heavy rain. in the 0520 heavy rain, the best threat score of the ensemble decision method was respectively 0.31, 0.25 and 0.36. In the 0517 heavy rain event, due to the rainfall conditions was stable, as the time was close to actual flood disaster occurred, the ensemble decision method had better mastery of the actual flood disaster amount. In the 0518 heavy rain event, there was one township with flood disaster. the decisive forecast and the ensemble decision method had insufficient mastery of flood disaster. In the 0520 heavy rain event, due to the unstable rainfall conditions and the flood warnings could not fully represent the actual flood disaster, the decisive forecast and the ensemble decision method had insufficient mastery of flood disaster. After the three heavy rain events in 2019, it is sufficient to prove that the flood pre-alert issued by the ensemble decision method can better grasp the actual flood disaster than the flood pre-alert issued by the decisive forecast. | en |
dc.description.provenance | Made available in DSpace on 2021-05-11T04:40:25Z (GMT). No. of bitstreams: 1 ntu-108-R06521611-1.pdf: 2544021 bytes, checksum: 4bb57324041ae0e5ba3e6b1118bf71d1 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 摘要 I
ABSTRACT II 目錄 IV 圖目錄 V 表目錄 VI 第一章 研究背景 1 第二章 文獻回顧 2 2.1 氣象預報的不確定性 2 2.2 防災決策作業 3 2.3 資料視覺化 5 第三章 研究目標 8 第四章 研究方法 9 4.1 建構防災資料模型 9 4.2 開發系集預警圖資 14 4.3 建立決策流程 16 第五章 研究實作 19 5.1 建構防災資料模型 19 5.2 開發系集預警圖資 21 第六章 驗證 26 6.1 2019年3場豪雨事件 26 6.2 驗證準備 32 6.3 驗證作業 34 6.4 結果討論 55 第七章 研究貢獻、限制與未來工作 61 7.1 研究貢獻 61 7.2 研究限制 62 7.3 未來工作 63 第八章 結論 64 參考文獻 65 | |
dc.language.iso | zh-TW | |
dc.title | 利用系集決策法將氣象預報不確定性導入防災決策 | zh_TW |
dc.title | Introducing Meteorological forecast uncertainty into disaster prevention decision Using Ensemble Decision Method | en |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 郭鴻基(Hung-Chi Kuo) | |
dc.contributor.oralexamcommittee | 蔡孟涵(Meng-Han Tsai),郭純伶(Chun-Ling Kuo) | |
dc.subject.keyword | 系集決策法,氣象預報不確定性,防災決策,資料視覺化, | zh_TW |
dc.subject.keyword | ensemble decision method,uncertainty of meteorological forecast,disaster prevention decision,data visualization, | en |
dc.relation.page | 67 | |
dc.identifier.doi | 10.6342/NTU201903895 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2019-08-20 | |
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 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-108-1.pdf | 2.48 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。