在氣候變遷的影響下，極端氣候與天然災害對社會經濟與自然環境之衝擊逐漸提升，且此趨勢遠非減少溫室氣體排放所能避免。因此，能夠精確辨識氣候風險並調整未來發展模式，增強系統韌性以抵抗極端氣候影響之「調適」策略，近年來逐漸受到各界重視，而地方機構自行制定因地制宜之調適計畫是未來國際趨勢。在調適策略的制定框架中，脆弱度評估係重要步驟，然而我國過去之天災脆弱度研究多針對都會區、環境保護區，且交通領域中亦鮮少針軌道系統脆弱度進行探討。
在全球、亞洲與我國的範疇下，颱風挾帶的強降雨、洪水、強風等災害（颱洪災害）皆是造成災害程度與損傷最大的災害類別，而本研究在比較未來災害情境變化後發現，高雄市將面臨更嚴重之災害，且為符合地方自訂調適之趨勢，故挑選高雄捷運系統作為颱洪災害脆弱度評估之研究對象。研究方法方面，鑒於我國調適相關資料較缺乏，故本研究使用修正式德爾菲法與貝氏網路作為主要研究方法，藉專家意見研擬脆弱度指標並建構模型，最終進行高雄捷運脆弱度分析，以及構面影響分析、情境分析之討論。
初始模型之分析結果顯示，強風顯著影響高架與平面段系統，洪水則顯著影響地下與平面段系統，表示本研究模型結果與數據符合捷運系統於實務中面臨之災害現況；在高捷系統颱洪災害脆弱度基礎模型中，高雄捷運系統呈現高脆弱度的機率為 69 %，表示當系統未來遭颱洪災害衝擊時，有較高機率會發生影響與損害事件，導致系統受損並僅能維持部分功能，甚至暫時中斷營運。
構面分析結果顯示，「承受與抵禦力」為對脆弱度影響最大之構面，其次為「敏感度」以及「調適與轉型力」構面，建議針對上述構面與指標優先改善或策略擬定。根據情境分析結果，若系統本身的硬體狀態較差時，則需要以較佳的人員能力來彌補缺失，否則高脆弱度機率值將顯著升高；換言之，若系統本身的承受與抵禦力充足，再擁有良好人員素質、完善規章、充分且及時之備援設備，則可將捷運系統脆弱度維持於較低的狀態。最後，本研究證實修正式德爾菲法與貝式網路能於軌道系統天災脆弱度領域使用，此二方法分別對於指標選取與定義、評估結果呈現，以及因果關係解釋，都有良好效果。但本研究發現其仍具部分限制與缺點，並提出建議供未來研究參考。

Under climate change, extreme weather events and disasters are increasingly impacting both society and the environment. Since reducing greenhouse gas emissions alone cannot prevent such risks, more attention is being given to adaptation strategies to enhance system resilience. Locally tailored plans led by municipal governments have become a global trend. A key step in these strategies is vulnerability assessment; however, in Taiwan, studies have primarily focused on cities, with limited emphasis on the transportation sector, particularly rail systems. Given that typhoons, which cause heavy rainfall, flooding, and strong winds, are among the most damaging disasters in Asia and Taiwan, this study assesses the Kaohsiung Metro's vulnerability to such hazards. Using the Modified Delphi Method and Bayesian Network modeling to address Taiwan's limited adaptation data, expert input was utilized to identify indicators and construct the model, which was applied in a case study of the Kaohsiung Metro.
Initial model results indicate that strong winds have a significant impact on elevated and at-grade sections, while flooding notably affects underground and at-grade sections, demonstrating that the model and data accurately reflect the actual disaster risks faced by metro systems. In the baseline vulnerability model for typhoon and flood disasters, the Kaohsiung Metro shows a 69% probability of high vulnerability, implying a high likelihood of experiencing impact and damage when exposed to such hazards in the future. These events could impair the system, resulting in reduced operational functionality or temporary service suspension.
The dimension analysis results indicate that Robustness and Resistance has the most significant impact on vulnerability, followed by Sensitivity, Adaptation, and Transformability. It is recommended to prioritize improvements and strategic planning focused on these dimensions and their associated indicators. According to scenario analysis, if the system's physical infrastructure is in poor condition, enhanced personnel capabilities are required to compensate for deficiencies. Otherwise, the probability of high vulnerability will increase significantly. In contrast, if the Kaohsiung Metro possesses adequate robustness and resistance, along with well-trained personnel, perfect regulations, and sufficient backup facilities, its vulnerability level can be maintained at a relatively low state.
Ultimately, this study confirms that the Modified Delphi Method and Bayesian Network are effective and practical tools for assessing vulnerability to natural disasters in rail transit systems. These two methods demonstrate strong capabilities in indicator selection and definition, result interpretation, and causal reasoning. Nevertheless, certain limitations and shortcomings remain, and the study proposes suggestions to guide future research.

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