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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔡丁貴(Tin-Kuei Tsay) | |
| dc.contributor.author | Fu-Ru Lin | en |
| dc.contributor.author | 林福如 | zh_TW |
| dc.date.accessioned | 2021-06-13T03:29:49Z | - |
| dc.date.available | 2008-07-31 | |
| dc.date.copyright | 2006-07-31 | |
| dc.date.issued | 2006 | |
| dc.date.submitted | 2006-07-27 | |
| dc.identifier.citation | Amani, A. and Lebel, T., (1997). “Lagrangian kriging for sahelian rainfall estimation at small time steps.”, J. Hydrol., 192: 125–157.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/32057 | - |
| dc.description.abstract | 近年來人工智慧之研究蓬勃發展，其中以類神經網路為最具代表性的成果之一。類神經網路分為監督式與非監督式架構，其中非監督式學習之網路架構具有相當高的容錯能力，在一個分類完成的架構下，即使輸入的資料有一部分殘缺不全，仍可由殘餘的資訊辨別出該筆資料所屬之類別，進而利用辨識的結果，作為輸出，成為預報的成果。針對此一特性，本研究提出結合型態辨識法與統計學上的集群分析來對集水區內降雨之空間與時間分佈分類以及進行預報，並進一步用於雨量資料的補遺，以期建立一套具通用性的預報模式。而且不論是那幾個雨量站有缺資料，模式都能進行雨量資料的預報。
本研究基於地理環境與氣象因素的考量，假設降雨在空間上與時間上之分佈可能存有某幾種特定的類型，而提出一套以型態辨識法結合集群分析來建構降雨預報模式的程序。利用前一段時間之降雨在空間上與時間上之分佈，即可預報集水區各站下一個小時的降雨情形。無需氣象、氣候等觀測條件。經過驗證與測試，本模式用於淡水河流域之雨量預報具有相當良好之結果。當新的降雨型態被納入模式辨識時，模式也能顯示更好結果。 | zh_TW |
| dc.description.abstract | Recently, the research of artificial intelligence is full of vitality. The Artificial Neural Networks (ANNs) is one of the most representative of achievements. The ANNs can divide into two kinds: supervised learning and unsupervised learning. Unsupervised learning has powerful ability of holding error. A clustered construction, even the input data is imperfect, still can identify the coordinate data from the remainder data. Take the result of recognition into output, it will become the output of forecasting. In connection of the characteristic, the research brings up the pattern recognition and cluster analysis in statistics to classify the rainfall in space and time and to forecast. It is intended to build an all-purpose forecast model. Whenever any data in rain gage is missing, the model also can hold on.
Based on the consideration of factors in meteorology and geography, this research assumes that rainfall exist certain pattern of space and time. A procedure of rainfall forecast of model construction is proposed. Only rainfall data of space and time in the previous time are needed to forecast the condition of rainfall next time steps. It doesn’t need any other condition in meteorology and in climate. Present proposed forecast model is tested using historical rainfall data in Danshui River basin. Reasonably good results have been observed. It indicates that the model will perform better every time when new rainfall patterns are integrated. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T03:29:49Z (GMT). No. of bitstreams: 1 ntu-95-R93521318-1.pdf: 674005 bytes, checksum: 4c979898c9a815efddd9510b77f5bb17 (MD5) Previous issue date: 2006 | en |
| dc.description.tableofcontents | 摘要 I
ABSTRACT II 目錄 III 圖目錄 V 表目錄 VIII 第一章 緒論 1 1.1 前言 1 1.2 文獻回顧 1 1.3 研究目的 4 1.4 論文架構 5 第二章 研究方法 7 2.1 集群分析簡介 7 2.1.1 基本概念 7 2.1.2 決策流程 8 2.1.3 相似性衡量 9 2.1.4 集群方法的選擇 10 2.2 型態辨識法理論說明 17 第三章 模式架構與驗證 20 3.1 模式架構 20 3.2 模式驗證方法 22 3.3 模式應用 23 第四章 實際案例 27 4.1 研究區域 27 4.2 颱洪事件的選定 29 4.2.1 率定組 29 4.2.2 驗證組 31 4.3 研究結果 31 4.3.1 最佳網路架構下之率定結果 38 4.3.2 最佳網路架構下之驗證結果 45 4.4 其他測試 68 第五章 結論與建議 78 5.1 結論 78 5.2 建議 79 參考文獻 80 | |
| dc.language.iso | zh-TW | |
| dc.title | 結合型態辨識與集群分析在定量降雨預報之研究 | zh_TW |
| dc.title | A Study on Pattern Recognition and Cluster Analysis for Rainfall Forecasting | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 94-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 徐年盛,張斐章 | |
| dc.subject.keyword | 類神經網路,型態辨識,集群分析,淡水河流域,降雨預報, | zh_TW |
| dc.subject.keyword | ANNs,pattern recognition,cluster analysis,Danshui River basin,rainfall forecast, | en |
| dc.relation.page | 84 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2006-07-28 | |
| 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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