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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 韓仁毓 | zh_TW |
dc.contributor.advisor | Jen-Yu Han | en |
dc.contributor.author | 吳宸瑋 | zh_TW |
dc.contributor.author | Chen-Wei Wu | en |
dc.date.accessioned | 2023-08-15T17:49:23Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-08-15 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-07 | - |
dc.identifier.citation | 109氣象年報. (2020).
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88796 | - |
dc.description.abstract | 現今全球都因氣候異常而面臨許多災害問題,而這些災害中又以與降雨有關的災害佔最大比例。世界各國為了降低這些因降雨而導致的損失以及增加面對災害時的應變能力,各自都有不同的作為。其中又以成本效益高、施行時間快速的非結構性措施最受各國青睞。為了使非結構性措施能夠有效運用於防災救災,獲取各地正確的降雨是至關重要的依據。目前獲取雨量資訊的主要工具分為雨量站及雷達回波,雨量站能夠直接蒐集雨量資訊,提供準確的點狀資料;雷達回波則是採用間接觀測的方式,將蒐集到的反射率轉換為降雨率,提供具參考性的面狀資料。本研究將利用上述之蒐集與量工具並考慮了其他可能對降雨會產生影響的因子,透過深度學習的方式建立較高準確度的雨量推估模型,也將討論時間序列的模型對於降雨量的推估是否會優於非時間序列模型,透過分別建立非時間序列模型多層感知器(Multi – Layer Perceptron, MLP)及時間序列模型長短期記憶(Long Short – Term Memory, LSTM),得出在記憶時間合宜的情況下,時間序列模型能有更好的結果。針對合宜的時間模擬了18個小時內的情境,得到只記憶4個小時以內的結果與非時間序列模型的結果無顯著上的差異,透過因子重要性排序發現原因為先驗值的貢獻程度低,導致無法發揮時間序列模型的優勢;而記憶至少14個小時的結果則為最佳。另外根據因子重要性排序得到,影響雨量值的主要因素包含經緯度及月份,透過與中央氣象局發行的氣候年報比對,證實了降雨與上述因子確實有著相當的關連性。經上述研究證實,時間序列模型相較於非時間序列模型,對於大雨等級以下的雨量推估有較好的結果,此外將影響降雨的因子納入模型中也能幫助獲得更準確的資訊。 | zh_TW |
dc.description.abstract | Nowadays, the world is facing numerous disaster issues due to abnormal climate conditions, with rainfall-related disasters comprising the largest proportion among them. Countries around the world have taken various measures to reduce losses caused by rainfall and enhance their resilience in the face of disasters. Nonstructural measures that are cost-effective and can be implemented quickly have gained popularity among countries. To effectively utilize nonstructural measures for disaster prevention and mitigation, obtaining accurate rainfall data is crucial. Currently, the primary tools for obtaining rainfall information are rain gauges and radar echoes. Rain gauges directly collect rainfall information and provide accurate point data; radar echoes, on the other hand, employ an indirect observation method by converting collected reflectivity into rainfall rates, providing informative spatial data for reference. Besides two conventional tools that collect rainfall values, this study also considers other factors that may have an impact on rainfall. This study also combines accurate rainfall station data, informative radar echoes, and multiple spatial-temporal factors affecting rainfall to establish a high-accuracy rainfall estimation model using deep learning techniques. Investigating whether time series models outperform non-time series models in rainfall estimation. Comparion is conducted by separately modeling a non-time series model MLP and a time series model LSTM. Concluded that the time series model demonstrates better performance in rainfall estimation. Furthermore, incorporating the factors influencing rainfall into the model also contributes to obtaining more accurate information. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T17:49:23Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-08-15T17:49:23Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vii 表目錄 ix 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 2 1.3 研究流程 4 1.4 論文架構 4 第二章 文獻回顧 6 2.1 影響雨量的因子 6 2.2 傳統雨量內插方法 7 2.2.1 徐昇式法 8 2.2.2 反距離權重法 8 2.2.3 克利金法 8 2.2.4 小結 9 2.3 氣象雷達的應用與特性 9 2.3.1 都卜勒雷達 10 2.3.2 雙偏極化雷達 11 2.3.3 臺灣地區 12 2.3.4 小結 13 2.4 機器學習與深度學習的應用 14 2.5 小結 15 第三章 研究方法 17 3.1 建立雨量推估的深度學習模型 17 3.1.1 多層感知器(Multi – Layer Perceptron, MLP) 18 3.1.2 長短期記憶(Long Short – Term Memory, LSTM) 19 3.2 影響降雨因子的資料前處理 22 3.2.1 計算雨量站點上的坡度值 23 3.2.2 將雨量站點上的坡向轉換為八方位 24 3.2.3 剔除資料缺失數過多的站點與雨量資料型態調整 25 3.2.4 轉換雷達回波值並將其由面狀資料轉為點狀資料 26 3.3 成果評估 27 3.3.1 比較長短期記憶模型中記憶不同時間段的精度 27 3.3.2 比較兩種模型的精度 27 3.3.3 探討在致災性降雨下模型的推估能力 28 3.3.4 影響降雨的因子重要性排序 29 3.4 小結 30 第四章 實驗成果與分析 31 4.1 研究區域與資料集 31 4.2 LSTM模型中記憶不同時間段成果 32 4.3 兩種深度學習精度比較 36 4.4 致災性降雨下模型表現成果 38 4.5 影響降雨的因子重要性排序 44 4.5.1 影響降雨的重要因子 45 4.5.2 推測僅記憶前4小時降雨模式表現不佳的原因 47 4.6 小結 47 第五章 結論與建議 49 5.1 結論 49 5.2 未來工作建議 50 參考文獻 52 | - |
dc.language.iso | zh_TW | - |
dc.title | 結合多維度時空因子與都卜勒雷達觀測於區域雨量推估 | zh_TW |
dc.title | Integrating Multiple Spatial-temporal Factors and Doppler Radar Observables for Regional Rainfall Estimation | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 甯方璽;莊子毅 | zh_TW |
dc.contributor.oralexamcommittee | Fang-Shii Ning;Tzu-Yi Chuang | en |
dc.subject.keyword | 雨量推估,多時空因子,都卜勒雷達,多層感知器,長短期記憶, | zh_TW |
dc.subject.keyword | Rainfall prediction,Multiple spatial-temporal factors,Doppler radar,MLP,LSTM, | en |
dc.relation.page | 55 | - |
dc.identifier.doi | 10.6342/NTU202303342 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-08-09 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 土木工程學系 | - |
顯示於系所單位: | 土木工程學系 |
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