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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 孫志鴻(Chin-Hong Sun) | |
| dc.contributor.author | Ming-Cheng Tsou | en |
| dc.contributor.author | 鄒明城 | zh_TW |
| dc.date.accessioned | 2021-06-13T17:27:25Z | - |
| dc.date.available | 2004-12-28 | |
| dc.date.copyright | 2004-12-28 | |
| dc.date.issued | 2004 | |
| dc.date.submitted | 2004-12-22 | |
| dc.identifier.citation | 中文部份
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/39390 | - |
| dc.description.abstract | 隨著資訊科技的進步,資料的收集紛紛邁入自動化與電腦化，造成空間資料的迅速累積，衛星遙測影像、GPS的資料收集、行動通訊裝備以及各種與位置相關的交易(Transaction)，提供我們大量具有空間參考的資料，坐擁如此龐大的資料，其中包含了許多寶貴的資訊與知識，如何從這些資料中提煉出有價值的知識，是當前面臨的一大課題。目前越來越受重視的資料探勘技術可以協助決策者從大量資料中找出有價值的知識，但是大部分的應用仍只限於屬性資料的分析，對於空間資料的處理分析少有著墨，而地理資訊系統雖然具有強大的空間資料處理能力，但缺乏對於屬性資料的進階處理。
有鑒於台灣地處地震帶，部分地區每年均要歷經多次的地震以及周期性的大地震，往往帶來嚴重的山崩以及土石鬆動的現象，若再歷經豪雨後，將帶來嚴重的土石流，造成生命財產的重大損失。民國88年9月21日於台灣中部地區發生芮氏規模7.3的地震，此次強震造成2000多人死亡，難以估計的財產損失，以及數量龐大的坡地災害。而在此次強震中亦獲得大量的坡地破壞資料及地震記錄，可供專家學者進行地震對山崩影響的研究，得以對地震引致山崩的行為有進一步的認識。 本研究嘗試以九二一大地震所累積的大批地理資料，結合地理資訊系統空間資料分析與處理的功能，建立資料倉儲作為資料探勘的基礎，分別以預測型資料探勘技術與描述型資料探技術，對地震山崩空間資料庫作全面性的知識探索。其中預測型資料探勘技術包含有類神經網路模式、決策樹模式、案例式概念學習及貝氏分類器等多個具互補性之資料探勘技術，分別探討各模式效能並評估其整合預測模式的建立。而描述型資料探勘則包含有線上分析處理(OLAP)、關聯法則及等級相關分析，用以找出引致地震山崩的推論法則及關聯樣式(association pattern)。所使用的資料素材則包含了向量式以及網格式資料，藉以探討各種資料探勘技術在空間資料庫知識發掘的適用性以及其整合研究，獲得良好的結果。期望透過此研究了解 引發地震山崩的機制，建立山崩災害機率的潛感圖以及知識庫和模式庫，並且探討各種資料探勘技術在空間資料庫知識探索上之應用性，提供防災決策支援上的參考。 | zh_TW |
| dc.description.abstract | With the progress of information science and technology, the data collection march toward the automation and computerization. The fast accumulation of the spatial data, such as satellite image, GPS data recorder, mobile communication equipment and various kinds of location-based transaction, offer a large number of geo-referenced data. There include a lot of valuable information and knowledge among these data. How to refine out valuable knowledge from these data is a great subject faced at present. Data mining can help the policymaker to find out valuable knowledge from a large amount of data, but most application are still only limited to the analysis of the attribute data. It is difficult to deal with the spatial data. And though geographical information system is powerful in analyzing spatial data, but it lack the advanced ability to deal with sophisticated attribute data analysis. Because Taiwan is located in the earthquake zone, some areas go through a lot of earthquakes and periodic heavy earthquakes every year and cause serious landslide. If after going through the torrential rain again, will bring serious debris flow and cause great losses of the lives and properties. An earthquake of magnitude 7.3 on Richter scale occurred in the middle region of Taiwan on September 21, 1999. This earthquake caused more than two thousand people died, severe property loss, and a large number of landslides. A large number of landslide data and earthquake strong motion records were obtained for the experts and scholars to carry on the research of landslide influence of the earthquake. This research collects data of landslides triggered by Chi-Chi earthquake, and with the powerful data-processing function and spatial analysis ability of Geographic Information System (GIS), Data Mining modeling, the basic data of research region, and Chi-Chi earthquake strong motion records to establish the landslide database and data warehouse. A new strategy, which
combines several models based on different philosophy, not only reduce the uncertainty of predictive modeling, but also improve the accuracy. In our study, a Decision Tree, Artificial Neural Network, Bayes Classfier, and Exemplar-based Concept Learning were individually applied to a spatial data warehouse. The result of each model and two kinds of modeling-integration methods, including horizontal integration and vertical integration, were then evaluated. Furthermore, the spatial association patterns are typically not encoded in database, but are rather embedded within the spatial framework of the geo-referenced data. The analysis of the association pattern between the occurrence of Chi-Chi earthquake-induced landslide and background environmental characteristics is used as a case study to demonstrate the potential of spatial data mining techniques, like OLAP, association rule mining and Spearman rank correlation. With the analysis results, we derived a suspecious potential map and build the knowledge base and model base. Verification proofed the result to be good. So the analysis methods mentioned by this research are suitable for the risk assessment of landslide hazard triggered by earthquake and can be used as the tool for disaster mitigation decision support. | en |
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| dc.description.tableofcontents | 第一章 緒 論 1
第一節 前 言 1 第二節 研究動機 3 第三節 研究目的 5 一、建立究區域自然環境之空間資料倉儲 5 二、建立資料探勘模式庫 5 三、比較與整合各資料探勘模式 5 四、建立防災知識庫、災害潛勢圖，提供決策支援參考 5 第四節 研究流程 7 一、文獻回顧 7 二、研究區域空間資料蒐集與建立 7 三、空間資料之分析處理 7 四、模式的選用與建立 7 五、模式驗證評估 8 六、模式庫與知識庫的建立 8 七、結論與建議 8 第二章 文獻回顧 9 第一節 知識探索(knowledge discovery)與資料探勘(data mining) 9 一、知識的定義 9 二、資料庫知識探索與資料探勘 10 三、資料倉儲 11 四、可發現的知識類型 13 五、資料庫知識探索的流程 16 第二節 空間資料探勘 23 一、地理資訊系統 23 二、資料探勘與地理資訊系統 23 三、空間資料庫知識探索 25 四、資料探勘與空間資料分析 26 五、空間資料探勘之目標 27 六、空間資料探勘架構 30 第三節 空間資料探勘之應用與研究 33 一、空間資料探勘的應用 33 二、空間資料探勘之未來研究需求 34 第四節 地震引致山崩影響因子研究 36 一、山崩與地質分佈狀況關係 36 二、山崩與地形因子的關係 37 三、山崩與斷層的關係 39 四、山崩與震央距離的關係 40 五、山崩與區位因子的關係 40 六、山崩與地震規模的關係 41 第五節 山崩發生可能性之研究 43 一、定性分析法 43 二、定量分析法 43 第三章 研究方法 48 第一節 研究架構 48 第二節 線上分析處理技術(On-line Analysis Process)─建模前分析 51 第三節 描述型資料探勘模式 58 一、關聯法則 58 二、Spearman 等级相關分析(Spearman Rank Correlation) 67 第四節 預測型資料探勘模式 69 一、決策樹演算法(Decision Tree) 69 二、類神經網路技術(Neural Network) 73 三、案例式概念學習(Exemplar-Based Concept Learning) 76 四、貝式分類器(Bayes Classfier) 78 五、預測型模式之整合 79 第四章 空間資料庫與資料倉儲之建立 90 第一節 山崩影響因子的選取 90 一、地形因子 91 二、地質因子 92 三、區位因子 92 四、地震影響因子 92 第二節 研究區域概述 93 一、地理位置 93 二、地形 94 三、地質 94 四、氣候 97 第三節 資料蒐集及處理 98 一、地震崩塌地資料之蒐集 98 二、數值地形模型之蒐集 99 三、車籠埔斷層分佈圖 99 四、集集大地震震央位置圖 99 五、道路分佈圖 100 六、活動斷層圖 100 七、水系分佈圖 100 八、數值地質圖 100 九、集集大地震地震紀錄 100 第四節 空間資料倉儲的建立 102 第五節 資料化簡與採樣 102 第五章 模式的評估 104 第一節 評估標準 104 一、錯差矩陣(confusion matrix) 104 二、累積增益圖(gain chart) 105 第二節 預測型模式之結果比較 109 第三節 預測型模式之整合比較 111 一、水平整合效能評估 111 二、垂直整合效能評估 111 第四節 空間背景(Spatial Context)資料的加入對於模式的影響 113 第六章 模式結果與應用 115 第一節 OLAP於向量資料的分析 115 一、從個別維度來分析 115 二、從多個維度來分析 115 第二節 Spearman等級相關分析 117 第三節 關聯法則之結果分析 130 第四節 決策樹演算法於網格式資料之分析 135 一、影響因子之分析 135 二、法則的產生 135 第五節 山崩潛感圖的製作 138 一、預測型模式之山崩潛感圖製作 138 二、關聯法則之山崩潛感圖製作 138 第七章 結論與建議 141 第一節 結論 141 一、方法論部份 141 二、地震引致山崩部份 143 第二節 後續研究建議 145 參考文獻 146 中文部份 146 英文部分 149 | |
| dc.language.iso | zh-TW | |
| dc.title | 空間資料庫知識探索之研究─以集集大地震引致之山崩為例 | zh_TW |
| dc.title | The Study of Knowledge Discovery from Spatial Database
Chi-Chi Earthquake-Induced Landslide As A Case Study | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 93-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 周天穎(Tien-yin Chou),朱子豪(Tzu-How Chu),賴進貴(Jinn-Guey Lay),周學政(Hse-Cheng Chou) | |
| dc.subject.keyword | 地理資訊系統,資料庫知識探索,資料探勘,地震引致山崩, | zh_TW |
| dc.subject.keyword | earthquake-induced landslid,knowledge discovery from database (KDD),data mining,geographic information system, | en |
| dc.relation.page | 156 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2004-12-22 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 地理環境資源學研究所 | zh_TW |
| 顯示於系所單位： | 地理環境資源學系 | |
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