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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張斐章(Fi-John Chang) | |
| dc.contributor.author | Jia-Yi Liou | en |
| dc.contributor.author | 劉佳儀 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:09:42Z | - |
| dc.date.available | 2021-09-02 | |
| dc.date.available | 2022-11-23T09:09:42Z | - |
| dc.date.copyright | 2021-09-02 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-19 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79747 | - |
| dc.description.abstract | 受到氣候變遷與都市化的影響，近幾年洪水災害有頻率增加、災害程度更加劇烈的趨勢，為減輕災害的損失，各國政府對於洪災預警與災害應變更加重視，以提早針對災害發生地區進行應變，藉此降低災害所造成的影響。 本研究蒐集近年來臺北市實際淹水事件之雨型，透過SOBEK二維淹水模式，模擬實際暴雨事件與設計暴雨事件(共51場)，得到2047筆模擬淹水資料，每筆資料包含45101個10m×10m的網格淹水深資料。使用主成分分析、自組特徵映射網路(SOM)與非線性自回歸外因輸入模式(R-NARX)針對抽水站集水區建立都市區域淹水預測模式；以淹水模擬資料進行主成分分析，透過四個主成分值代表不同淹水空間分布的特性；SOM根據各時刻淹水特性進行聚類分析，將淹水模擬網格資料映射到二維拓樸圖上，各神經元可以表示不同淹水大小與淹水空間分布之狀況；R-NARX以回饋項加上雨量資料作為輸入資料，分別建立以10分鐘為時距之未來一小時預測模式(T+1~T+6)，並且以兩種模式進行預測結果比較，模式一為預測平均淹水深的單輸出模式，模式二則是預測平均淹水深與主成分值的多輸出模式；最後之整合模式先將R-NARX預測結果比對SOM拓樸圖各神經元，模式一選擇與預測平均淹水深最相近的神經元，模式二選擇各主成分值最相近的神經元，以及被選總次數最高神經元，並將所選神經元依照不同比例計算各網格之權重，模式一與模式二選出神經元後，以平均淹水深預測結果微調各網格權重值，藉此得到區域淹水預測結果。 本研究分析結果顯示9×9拓樸圖大小較能完整呈現不同淹水空間分布；R-NARX建立之預測模式，比較模式一與模式二於平均淹水深之預測結果，可發現模式二之R2數值較小，模式二預測較難以掌握平均淹水深之趨勢，顯示多輸出模式因訓練時根據多個輸出項誤差調整參數，相較於單輸出模式預測結果較差；由模式二各主成分預測結果可知，部分主成分值因趨勢與平均淹水深較不同，因此預測模式較難以掌握其趨勢，於各預測時距之R2數值較小。比較模式一與模式二之整合模式結果，模式二於各預測時距之結果相較於模式一RMSE數值較小；並以SOBEK模擬淹水深與T+1模式一、模式二之預測淹水深進行比較，結果顯示模式一使用平均淹水深篩選神經元，較適合用於全面降雨的颱風事件，模式二使用各主成分值篩選神經元，因具有淹水空間分布特性之指標，較適合用於降雨空間分布不均的豪雨事件，相較於模式一有較穩定準確的淹水空間分布預測結果。 本研究結果顯示以主成分值代表淹水空間分布特性，並結合SOM與R-NARX模式，能夠掌握不同降雨空間分布所造成之淹水情形，並且即時提供都市區域的淹水預測，可以幫助決策者提前針對預測淹水地區進行應變。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:09:42Z (GMT). No. of bitstreams: 1 U0001-1808202115280500.pdf: 14601837 bytes, checksum: 63d778282d90f5cc9dcb813fe7e830da (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 謝誌 I 摘要 III Abstract V 目錄 VII 圖目錄 IX 表目錄 XI 一、前言 1 1.1 研究背景 1 1.2 研究動機與目的 1 1.3 論文章節架構 2 二、文獻回顧 3 2.1 主成分分析之應用 3 2.2 自組特徵映射網路之應用 4 2.3 非線性自迴歸外因輸入模式之應用 5 2.4 淹水預測之相關研究 6 三、理論概述 8 3.1 SOBEK二維淹水模式簡介 8 3.2 主成分分析 9 3.2.1 主成分分析計算 9 3.3 自組特徵映射網路 10 3.3.1 自組特徵映射網路架構 10 3.3.2 自組特徵映射網路演算法 11 3.4 非線性自迴歸外因輸入模式 14 3.4.1 非線性自迴歸外因輸入模式架構 15 3.4.2 R-NARX並聯架構學習演算法 16 四、研究案例 21 4.1 研究區域 21 4.2 資料蒐集 23 4.2.1 淹水模擬情境設定 24 4.3 研究架構 33 4.4 評估指標 35 五、結果與討論 37 5.1 淹水主成分分析 37 5.2 SOM淹水空間聚類模式 42 5.2.1 SOM模式參數設定 42 5.2.2 拓樸圖時空變化特性分析 48 5.3 淹水預測模式 55 5.3.1 R-NARX模式設定 57 5.3.2 模式一預測結果 59 5.3.3 模式二預測結果 62 5.4 整合模式 69 5.4.1 預測誤差評估 70 5.4.2 淹水模擬事件之各時刻預測結果評估 73 六、結論與建議 81 6.1 結論 81 6.2 建議 82 參考文獻 84 附錄A SOM(9 × 9)模式累積率分布盒鬚圖 90 附錄B 模式一T+2~T+5預測結果 92 附錄C 模式二訓練與驗證階段預測結果 96 | |
| dc.language.iso | zh-TW | |
| dc.subject | 淹水時空變化分析 | zh_TW |
| dc.subject | 都市區域淹水預測 | zh_TW |
| dc.subject | 非線性自回歸外因輸入模式(R-NARX) | zh_TW |
| dc.subject | 主成分分析(PCA) | zh_TW |
| dc.subject | 自組特徵映射網路(SOM) | zh_TW |
| dc.subject | Spatio-temporal analysis of inundation | en |
| dc.subject | Principal Component Analysis (PCA) | en |
| dc.subject | Self-Organizing Map (SOM) | en |
| dc.subject | Nonlinear Autoregressive with Exogenous Inputs (R-NARX) | en |
| dc.subject | Urban flood forecasting | en |
| dc.title | 結合機器學習及主成分分析建置都市區域淹水多時刻預測模式-以臺北市為案例 | zh_TW |
| dc.title | Explore Machine Learning and Principal Component Analysis to Construct a Multi-Step-Ahead Urban Flood Forecasting Model - Taking Taipei City as a Case Study | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張麗秋(Hsin-Tsai Liu),黃文政(Chih-Yang Tseng),劉振宇,張凱堯 | |
| dc.subject.keyword | 主成分分析(PCA),自組特徵映射網路(SOM),非線性自回歸外因輸入模式(R-NARX),淹水時空變化分析,都市區域淹水預測, | zh_TW |
| dc.subject.keyword | Principal Component Analysis (PCA),Self-Organizing Map (SOM),Nonlinear Autoregressive with Exogenous Inputs (R-NARX),Spatio-temporal analysis of inundation,Urban flood forecasting, | en |
| dc.relation.page | 110 | |
| dc.identifier.doi | 10.6342/NTU202102474 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-08-20 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
| 顯示於系所單位： | 生物環境系統工程學系 | |
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