降雨對南橫公路50至110K沿線山崩潛勢之影響

Influence of Rainfall on Landslide Susceptibility along the 50 to 110K Section of the Southern Cross Island Highway, Taiwan

10.29417/JCSWC.201409_45(3).0005

詹勳全(Hsun-Chuan Chan)；卓佳駿(Chia-Chun Cho)；張嘉琪(Chia-Chi Chang)；洪雨柔(Yu-Jou Hung)

羅吉斯迴歸 ； 南橫公路 ； 降雨強度 ； Logistic regression ； the Southern Cross Island Highway ； rainfall intensity

中華水土保持學報

45卷3期（2014 / 09 / 01）

184 - 197

繁體中文

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，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，首先初選高程類因子、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。再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，探討降雨對於山崩潛勢分析的影響。整體結果顯示，於本研究山崩潛勢分析流程下，誘發因子的加入可增進模式對長延時高強度降雨型態（如：莫拉克颱風事件）的預測能力，針對中延時高強度與短延時中強度降雨型態模式，模式以潛在因子便足以維持一定的預測水準，誘發因子的加入對預測能力的提生並無顯著影響。

This study uses the inventories of landslides during typhoon Mindulle, Morakot, and the 07/19 rainfall event established by Central Geological Survey as landslide data. The elevation, slope, slope aspect, slope high, lithology, terrain roughness, slope roughness, plan curvature, profile curvature, total curvature, distance from the road, and distance from the river are first chosen as the landslide causative factors, based on previous studies. Secondly, calibration and selection procedures are performed to efficiently select the factors. Logistic regression method is used to establish the landslide susceptibility model. Furthermore, the rainfall intensities of different rainfall durations are used as a landslide triggering factor in different rainfall events. The maps of potential landslides are delineated to discuss the influence of rainfall on landslide susceptibility analysis. The landslide susceptibilities are separated into four levels, including high, medium, low, and steady. According to the results, the model adopting the proposed rainfall factor increased the landslide predictive capability for long-duration and high-intensity rainfall events, such as the Morakot event. For the other two events, similar landslide predictive capabilities are obtained with and without applying the landslide causative factor in the model.

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