當大規模天然災害發生後，由於部分道路遭受毀損，使部分孤島區域無法與外界相連，往往無法立即進行資源分配、災民疏散及搜救任務等工作，因此災前之減災及整備為災害管理中必要之任務。由於過去關於災害脆弱度之相關研究尚未見其針對社會脆弱度之探討及應用，本研究結合交通路網及社會脆弱度之概念，提出一風險區域分群模型，其中本研究與國家災害防救科技中心（NCDR）合作，例用該中心之地震衝擊資訊平台（TERIA）進行歷史地震之模擬，並結合交通路網資料產生地震後之災後路網，該災後路網與最小統計區比對後可產生該地震模擬後之孤島區集合，進而經過數千次歷史地震的模擬，即可獲得一最小統計區之潛在孤島區發生機率。將最小統計區之社會經濟資料結合該機率，並導入本研究利用統計方法訂定之潛在風險指標評估式，即可獲得一最小統計區之潛在風險指標，該指標經過轉換，透過本研究運用之三種分群演算法進行分群後，得以產生風險區域分群，提供決策者進行減災規劃之決策支援工作。本研究亦針對個案進行分群結果之統計指標分析，發現K-means演算法為具有強健性之分群演算法，而DBSCAN提供範圍較小且去除噪點之分群，階層式分群（Hierarchical Clustering）則是提供具有階層關係之分群結果。此外，本研究亦提出一決策支援流程，以提供決策者於特定情境下選擇合適的減災區域規劃。

After the occurrence of natural disaster, several isolated areas are unable to reach the medical facilities because of the malfunction of roads. The allocation of resources and rescue task is shut down consequently. As a result, mitigation and preparedness is an essential task before disaster in disaster management. Because there isn’t any discussion or application of disaster vulnerability combined with social vulnerability, this study will introduce a risk area clustering model including traffic vulnerability as well as social vulnerability. This study is cooperated with National Science and Technology Center for Disaster Reduction（NCDR）, whose Taiwan Earthquake impact Research and Information Application platform（TERIA）is for the simulation of earthquakes. Then, we combine the data of traffic network and the result of simulation in order to produce the post-disaster road network. The post-disaster network can be transferred into the isolated area so that we can acquire the potential probability of area being isolated. By the evaluation function of potential risk index, we can acquire the Potential Risk Index of Basic Statistical Area（BSA）after importing the probability of area being isolated and the social statistical data of Basic Statistical Area. Using the transformation of the Potential Risk Index, we use three kinds of clustering algorithm to produce different risk area cluster so that the decision maker can implement the task of mitigation plan. This study also do empirical study by using statistical index to compare different clustering algorithm. It finds out that K-means clustering is a robust algorithm, and DBSCAN clustering can provide a small and no-noise cluster result. Hierarchical Clustering can provide a cluster result with hierarchical relationship. Besides, this study also provides a decision workflow for decision maker to choose an appropriate mitigation area plan in specific scenario.

致謝--------------------------2
摘要--------------------------3
Abstract--------------------------4
第一章、緒論--------------------------9
1.1研究背景與動機--------------------------9
1.2研究目的--------------------------9
1.3論文架構--------------------------10
第二章、文獻回顧--------------------------12
2.1災害管理--------------------------12
2.2災害管理決策支援系統之應用--------------------------14
2.2.1多災害導向災害管理決策支援系統--------------------------15
2.2.2最佳化導向災害管理決策支援系統--------------------------16
2.3災後交通路網--------------------------17
2.3.1交通路網的特性--------------------------17
2.3.1.1可靠度（Reliability）--------------------------18
2.3.1.2脆弱度（Vulnerability）--------------------------19
2.3.1.3彈性（Resilience）--------------------------20
2.3.2交通路網的脆弱度--------------------------20
2.3.2交通路網於災害管理之應用--------------------------22
2.4社會脆弱度--------------------------22
2.4.1社會脆弱度評估架構--------------------------23
2.4.2社會脆弱度指標建立方法論--------------------------26
第三章、研究方法--------------------------28
3.1孤島區域模型建構--------------------------29
3.1.1災後路網模型建構--------------------------29
3.1.2潛在孤島區發生機率計算--------------------------31
3.2潛在風險指標設計--------------------------32
3.2.1潛在風險指標框架--------------------------33
3.2.2潛在風險指標評估式--------------------------33
3.3風險區域分群模型建構--------------------------35
3.3.1以距離為基礎之分群模型--------------------------36
3.3.2以階層為基礎之分群模型--------------------------38
第四章、個案探討--------------------------40
4.1個案背景--------------------------40
4.2潛在風險指標計算--------------------------42
4.3風險區域分群結果--------------------------45
4.3.1Hierarchical Clustering演算法分群結果--------------------------45
4.3.2 K-means演算法分群結果--------------------------49
4.3.3 DBSCAN演算法分群結果--------------------------56
4.4風險區域分群績效分析--------------------------60
4.5決策支援流程建構--------------------------66
4.5.1決策支援流程--------------------------66
4.5.2決策支援流程示例--------------------------68
4.6個案總結--------------------------69
第五章、結論與未來研究--------------------------71
參考文獻--------------------------72

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