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標題: 降雨對山崩潛勢分析之影響-以南橫公路50至110k沿線為例
Influence of Rainfall on Landslide Susceptibility Analysis–A Case Study along 50 to 110k Section of the Southern Cross Island Highway, Taiwan
作者: 洪雨柔
Hong, Yu-Jou
Contributors: 詹勳全
水土保持學系所
關鍵字: 羅吉斯迴歸;南橫公路;降雨強度
Logistic regression;the Southern Cross Island Highway;rainfall intensity
日期: 2013
Issue Date: 2013-11-07 13:17:09 (UTC+8)
Publisher: 水土保持學系所
摘要: 南橫公路橫貫中央山脈南脊,橫跨丘陵、縱谷與高山等地形,沿線豐富景觀乃重要自然資產,亦為南部地區東西往來之重要通道。近年來南部地區飽受颱風豪雨侵襲,造成南橫公路多次道路邊坡坍塌而阻絕交通,甚至危及用路人的安全,因此本研究以南橫公路50至110k之路段沿線上下邊坡1400公尺為研究對象,利用不同降雨事件建立山崩潛勢模式,提出高崩塌潛勢路段以預先發佈相關警戒,期望可降低災害帶來的損失。
本研究蒐集中央地質調查所製作之2004年敏督利颱風、2009年莫拉克颱風及2011年7月19日豪雨三場事件的事件型山崩目錄作為分析山崩組樣本,並初選高程類因子、坡度類因子、坡向、全坡高、岩性、地形粗糙度、坡度粗糙度、平面曲率、剖面曲率、總曲率、道路距、水系距等潛在因子,投入因子複選流程,篩選出不同事件下對於山崩鑑別能力較好的潛在因子,將各因子分為山崩組斜坡單元與非山崩組斜坡單元並進行正規化處理,以降低因子間值域與單位的差異。運用統計方法中的羅吉斯迴歸法建立潛在因子之山崩潛勢分析模式,最後加入各事件不同延時下的平均降雨強度作為誘發因子,將道路沿線之山崩潛勢值分為穩定、低、中及高崩塌潛勢區四個等級,並探討降雨對於山崩潛勢分析的影響。
在各事件加入誘發因子後均有70%以上的山崩預測能力,但敏督利事件與20110719豪雨事件的整體預測能力並無明顯提升,而莫拉克事件建模之整體正確率由66.82%增進至71.34%,顯示在本研究山崩潛勢分析流程下,長延時高強度降雨型態的莫拉克事件加入誘發因子增進模式預測能力的效果最為良好,其餘兩場事件均以潛在因子便足以維持一定的預測水準。
The Southern Cross Island Highway was suffered from the landslide disaster in recent years. In order to reduce the reconstruction cost of broken road, the landslide susceptibility models were established in this study. The mileage ranging from 50 to 110 kilometers and buffer range of 1,400 meters of the Southern Cross Island Highway was selected as the study areas.
The inventories of landslide during typhoon Mindulle, Morakot, and 0719 rainfall event by Central Geological Survey were selected as the landslide data. The elevation, slope, slope aspect, slope high, lithology, terrain roughness, slope roughness, plan curvature, profile curvature, total curvature, distance of road, and distance of river were first chosen as the landslide causative factors, according to the previous studies. Secondly, the calibration and selection procedure were performed to select the factors efficiently. Each factor value was normalized from 0 to 1 for reducing the difference between the range and unit of various factors. Logistic regression method was used for establishing the landslide susceptibility model. Furthermore, the rainfall intensities of different rainfall duration were used as a landslide triggering factor in different rainfall events. The maps of potential landslide were delineated to discuss the influence of rainfall on the landslide susceptibility analysis. The landslide susceptibilities were separated into four levels, including high, medium, low, and steady. The classification error matrix and ROC-curve were used to evaluate the accuracy of landslide predicted by the present model.
As a result, the present model predicted landslide effectively. The overall accuracy of three rainfall events was higher than 70%. However, the results of Mindulle and 0719 rainfall event did not improve obviously by using landslide triggering factors. The overall accuracy of Morakot event increased 66.82% to 71.34% after considering the landslide triggering factor. Namely, the model with the rainfall factor increases the landslide predictive capability, once the rainfall event belonged to the type of long-duration and high-intensity, such as Morakot event. The other two events remain similar landslide predictive capability with and without the landslide causative factor in the model.
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