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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 譚義績 | |
| dc.contributor.author | Yun-Chun Chen | en |
| dc.contributor.author | 陳(石勻)(女勻) | zh_TW |
| dc.date.accessioned | 2021-06-17T04:55:33Z | - |
| dc.date.available | 2019-08-01 | |
| dc.date.copyright | 2018-08-01 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-07-27 | |
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[34] 台灣颱風洪水研究中心，「典寶溪及宜蘭河防災測試基地監測及加值應用研究」， 經濟部水利署水利規劃試驗所，2015。 [35] 張斐章，張麗秋，2015，類神經網路導論-原理與應用第二版，滄海圖書 [36] 許銘熙，鄧慰先，黃成甲，1996，「八掌溪流域洪水及淹水預報模式之研究(二)」，行政院國家科學委員會報告 [37] 傅金城, 張駿暉, 葉森海, 黃成甲, 謝龍生, 游保杉, 葉克家和許銘熙，2010， 「淹水災害預警技術」，國研科技(25), pp.15-27. [38] 賴進松，張向寬，2001，「防洪示範區淹水境況模擬與決策支援系統之研究(一)-子計畫八：基隆河流域颱洪發生潰堤災害之境況模擬」，行政院國家科學委員會專題研究計畫 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71148 | - |
| dc.description.abstract | 颱風襲臺常造成淹水的災害，過去利用二維淹水模式產生淹水資料結果相當費時，無法在颱風期間及時演算，本文首先利用SOBEK產生大量淹水資料當作訓練資料，結合k-means聚類法和新型類神經網路-支援向量機(support vector machines, SVM)發展一套颱風淹水預警系統。
主要架構分成三部分：分類、預報以及空間推估，首先先區分出淹水區，在將淹水區的資料利用k-means聚類法以不同的淹水歷線型態進行分類，根據地理空間特性找尋鄰近的淹水監測站作為控制點。接著，在每個控制點建構預報模式，利用降雨量和水位兩個因子作為SVM預報模式的輸入項，預報控制點未來1至3小時的水位，將水位轉換成水深後，接著將各控制點預報的水深、二度分帶座標(X, Y)、雨量、9個淹水影響因子，分別是高程、坡度、坡向、總曲率、平面曲率、剖面曲率、與河川的距離、地形濕度指數(Topographic wetness index, TWI)、逕流強度指數(Stream power index, SPI)當作輸入項，利用SVM空間推估模式，即可推估未來1至3小時淹水區網格點的淹水深度。 本研究以宜蘭縣的宜蘭河流域與美福大排來驗證所提出的方法，結果顯示此方法能夠準確的預報未來1至3小時的淹水深度，以地理資訊系統(geographic information system, GIS)繪製各網格點的預報淹水深度，預報結果的淹水深度圖能夠反應出所收集到的淹水潛勢圖資料。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2021-06-17T04:55:33Z (GMT). No. of bitstreams: 1 ntu-107-R05622013-1.pdf: 42425877 bytes, checksum: 0895d3fff58ed14bc6b440bc4792cc70 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vii 表目錄 xii 第1章 緒論 1 1.1 研究動機 1 1.2 文獻回顧 3 1.3 研究目的 6 1.4 論文架構 7 第2章 理論與方法 9 2.1 聚類演算法 9 2.1.1 k-means聚類法 9 2.2 SOBEK模式 11 2.3 SVM-支援向量機 12 第3章 研究區域與資料蒐集 16 3.1 研究區域 16 3.2 淹水資料蒐集 17 3.3 淹水影響因子資料 23 第4章 模式建立與應用 31 4.1 颱風淹水預警系統 31 4.1.1 SOBEK模式建立 32 4.1.2 k-means分類 34 4.1.3 控制點預報模式 36 4.1.4 空間推估模式 37 4.2 交替驗證與評鑑指標 39 4.2.1 交替驗證 39 4.2.2 評鑑指標 39 第5章 結果與討論 40 5.1 SOBEK模式檢定驗證結果 40 5.2 k-means分類結果 50 5.3 控制點預報結果 57 5.4 空間推估結果 67 第6章 結論與建議 93 6.1 結論 93 6.2 建議 94 參考文獻 95 附錄 99 | |
| dc.language.iso | zh-TW | |
| dc.subject | SOBEK | zh_TW |
| dc.subject | 淹水影響因子 | zh_TW |
| dc.subject | 空間推估 | zh_TW |
| dc.subject | 水位預報 | zh_TW |
| dc.subject | 颱風淹水預警系統 | zh_TW |
| dc.subject | 支援向量機 | zh_TW |
| dc.subject | k-means聚類法 | zh_TW |
| dc.subject | k-means clustering | en |
| dc.subject | flood causative factors | en |
| dc.subject | SOBEK | en |
| dc.subject | spatial estimation | en |
| dc.subject | water level forecasting | en |
| dc.subject | typhoon inundation warning system | en |
| dc.subject | support vector machine | en |
| dc.title | 結合水理模式和機器學習法發展颱風淹水預警系統之研究 | zh_TW |
| dc.title | Development of A Typhoon Inundation Warning System by Hydraulic Routing Model and Machine Learning Algorithm | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 賴進松,陳建謀,張向寬 | |
| dc.subject.keyword | SOBEK,k-means聚類法,支援向量機,颱風淹水預警系統,水位預報,空間推估,淹水影響因子, | zh_TW |
| dc.subject.keyword | SOBEK,k-means clustering,support vector machine,typhoon inundation warning system,water level forecasting,spatial estimation,flood causative factors, | en |
| dc.relation.page | 119 | |
| dc.identifier.doi | 10.6342/NTU201802006 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-07-30 | |
| 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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