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  • 學位論文

降雨誘發之坡地崩塌災損評估

Assessment of Disaster Losses due to Rainfall-induced Landslide

指導教授 : 陳怡睿

摘要


臺灣平原開發日漸飽和,坡地的開發利用成為未來的趨勢,但近年來極端降雨事件,加上地震的影響,更容易引發坡地崩塌現象,進而影響生命財產安全。因此,山坡地的崩塌潛勢及災害損失評估,成為防災工作重要的一環,而且研究結果也可提供政府或相關防救災單位擬定治理對策之參考。 本研究以南臺灣南化水庫集水區之部分區域為範圍,運用基因演算自動演化類神經網路技術,針對研究區歷經莫拉克颱風侵襲前後之衛星影像,選定包含道路、建物、果樹、農田、綠覆、裸露地、河道、水域等圈繪樣區,進行分類判釋,以獲得地表變遷及災害資訊,並結合數值高程模型及雨量資料,運用地理資訊系統平臺,建置山崩潛勢因子資料庫。研究中,運用最佳數值搜尋原理所建置之崩塌潛勢評估模組,推估崩塌機率。此外,藉由判釋結果、實地勘查及政府公布的災害損失相關資料,進行建物、農地、林地及交通水利用地等,各類災害損失價值之推估,再透過地理資訊系統平臺,繪製區域山崩災害機率圖及災害損失推估圖。 研究結果顯示,衛星影像之判釋結果精確度達中高程度以上。研究區山崩潛勢之影響以坡地擾動程度及有效累積雨量影響較大,高程、坡地粗糙度及坡度次之,模式之正確率為70%。因此,本研究所推算之災害損失評估模式具有一定合理性,可提供政府及相關防救災單位做為災害預防、災害保險及災害損失救助金額擬定對策等之參考。

並列摘要


Taiwan’s plain development is increasingly saturated, slope land development become a future trend. However, landslides often occur due to extreme rainfall events and earthquakes, and then affect life and property safety. Therefore, assessment of landslide potential and disaster losses is an important part of disaster prevention. The research results also provide the government to develop anti-disaster countermeasures. The study area in this research is the Nanhua Dam watershed in the southern Taiwan. The satellite images before and after the 2009 Typhoon Morakot were acquired and used. Sample regions of roads, buildings, fruit trees, farmland, green cover, bare land, rivers, and waters were selected and the Genetic Adaptive Neural Network (GANN) was implemented in the analysis techniques for the interpretation of satellite images and to obtain surface information and hazard log data. The geographic information system combined with digital elevation model and rainfall data was employed to establish database of influencing factors of landslide. This study developed an evaluation module for landslide potential according to optimum seeking theory to predict landslide probability. Disaster losses in buildings, farmland, woodland, and transport and water conservancy land were estimated according to interpretation results, field investigation, and related information published by the government. The maps associated with the probability of landslide and prediction of disaster losses were plotted using the geographic information system. The results of image classification show that the values of coefficient of agreement are at medium-high level. The results show that weights of slope disturbance and effective accumulative rainfall are the highest, followed by weights of elevation, slope roughness, and slope. The correct rate of the model is 70%. Therefore, the proposed model can be applied in practice for subsequent disaster prevention, disaster insurance, and subsidy amount.

參考文獻


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