臺灣博碩士論文加值系統

事件型山崩潛感模型是利用事件誘發山崩目錄及地形、地質因子和促崩因子訓練建模。若建立模型的事件之前有超大豪雨或颱風或大地震發生過，事件誘發山崩的分布特性可能與未受到干擾的情況有所不同。本研究訂定時隔6個月以上第一場超過降雨門檻的雨量事件為獨立事件，在曾文水庫集水區挑選賀伯颱風、桃芝颱風、敏督利颱風、20050615豪雨、20060609豪雨、莫拉克颱風、20110718豪雨、20120610豪雨、20150523豪雨共九期獨立事件建立山崩潛感模型。將模型進行交叉驗證，發現以豪雨事件建立的模型彼此之間相互預測結果表現不錯。敏督利模型由於坡度因子及促崩因子表現良好，預測其他事件的AUC皆達到0.710以上，解釋莫拉克颱風誘發山崩的AUC達0.673，為最穩定的事件型山崩潛感模型。
比較歷年山崩目錄按傳統方法建立之不包括促崩因子的山崩潛感模型與各個事件基礎山崩潛感圖，可以觀察到九個事件個別建立的基礎山崩潛感圖與歷年山崩潛感圖會有相近的潛感分布趨勢，表示歷年山崩潛感圖即可代表一個區域的基礎潛感値分布。本研究選用歷年山崩潛感模型為基礎潛感模型，將各事件分為極端事件及一般事件，將不同潛感區間中相同雨量條件下的崩壞比取其平均值，分別建立山崩潛感‒崩壞比和最大時雨量/總雨量關係式，利用雨量因子間的相關係數將關係式合併以計算在不同雨量分布下各個潛感體質的崩壞比。各事件山崩機率圖的預估山崩面積與事件誘發山崩目錄比較結果顯示，本研究建立之關係式低估桃芝颱風誘發山崩；高估賀伯颱風、敏督利颱風及豪雨事件誘發山崩，預測莫拉克颱風誘發山崩與實際誘發山崩面積較為吻合。

An event-based landslide susceptibility model is constructed based upon an event landslide inventory, topographic factors, geological factors and triggering factors. If an extreme rainfall, typhoon, or a major earthquake happened before a modeling event, the characteristics of landslide distribution may be different with undisturbed conditions. In the present study, an independent event is defined as the event without a prior-event which exceeds the rainfall threshold of the region within 6 months or more. In the Zengwen reservoir catchment area, this study chooses nine independent events, including Herb typhoon, Toraji typhoon, Mindulle typhoon, 20050615 rainfall, 20060609 rainfall, Morakot typhoon, 20110718 rainfall, 20120610 rainfall, and 20150523 rainfall, to establish nine event-based landslide susceptibility models. These models are then cross-validated. The results are good between rainfall events. Due to good performance of slope factor and triggering factors, Mindulle model is the most stable event-based landslide susceptibility model for typhoon events. It shows that AUC of the prediction curve for landslide induced by Morakot typhoon is 0.673; AUCs of prediction curve for other events are more than 0.710.
Compare the landslide susceptibility model built by multi-temporal landslide inventories, via traditional approach without triggering factors, to the nine basic susceptibility maps built by each event, a similar trend of susceptibility distribution among them can be observed. This study chooses the multi-temporal landslide susceptibility map as representative basic susceptibility of the region. The events are divieded into extreme events and common events. In each susceptibility bin and each rainfall bin, the average of probability of landslide failure is calculated from every events landslide inventory of same type, and then a relationship among landslide susceptibility, probability of failure, and rainfall intensity or total rainfall is completed. Utilizing the correlation coefficient between two rainfall factors, this study combines the two relationships to calculate the probability of failure of different susceptibility values in rainfall event. Comparing the result of landslide area predicted by the relationship and each event inventory shows that the relationship underestimate landslide area of Toraji typhoon, and overestimate landslide area of Herb typhoon, Mindulle typhoom and rainfall events. Predicted landslide area of Morakot typhoon is approximate to actual landslide area.

摘 要 I
英文摘要 III
致謝 V
目錄 VI
圖目錄 IX
表目錄 XIII
第一章 緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 1
1.2.1 羅吉斯迴歸應用於山崩潛感分析 2
1.2.2 事件型山崩潛感分析 3
1.2.3 降雨量與崩壞比關係探討 4
1.3 研究架構與流程 6
第二章 研究方法 8
2.1 羅吉斯迴歸 8
2.1.1 羅吉斯迴歸之理論 8
2.2 驗證方法 12
2.2.1 分類誤差矩陣 12
2.2.2 成功率曲線與預測率曲線 13
2.3 事件獨立型山崩潛感模型 14
2.4 直方圖匹配 14
2.5 基礎潛感值暨雨量和崩壞比關係式 14
第三章 研究區域與研究資料 18
3.1 研究區域概述 18
3.1.1 位置 18
3.1.2 地形 18
3.1.3 水文 18
3.1.4 地層、構造概述 19
3.2資料蒐集與整理 23
3.2.1 地形資料及雨量資料和衛星影像 23
3.3 獨立事件選取 24
3.4 山崩目錄建立 27
3.4.1 山崩目錄判釋與檢核 27
3.4.2 誘發山崩屬性 28
3.5 雨量因子處理 34
3.5.1 雨量資料檢核 34
3.5.2 雨量分布空間推估 34
3.6山崩因子處理 41
3.7 因子篩選 47
3.7.1 山崩與非山崩次數分布圖 47
3.7.2 崩壞比圖 48
3.7.3 判別子（discriminator） 48
3.7.4 成功率曲線 48
3.7.5 P-P機率圖 48
3.7.6 相關係數 49
3.7.7 因子選取結果 49
第四章 事件型山崩潛感分析 97
4.1 分析樣本選取 97
4.2 山崩潛感分析模型建立 100
4.3 山崩潛感分析結果 101
4.3.1 賀伯事件山崩潛感模型結果 101
4.3.2 桃芝事件山崩潛感模型結果 105
4.3.3 敏督利事件山崩潛感模型結果 109
4.3.4 20050615豪雨事件山崩潛感模型結果 113
4.3.5 20060609豪雨事件山崩潛感模型結果 117
4.3.6 莫拉克事件山崩潛感模型結果 121
4.3.7 20110718豪雨事件山崩潛感模型結果 125
4.3.8 20120610豪雨事件山崩潛感模型結果 129
4.3.9 20150523豪雨事件山崩潛感模型結果 133
4.3.10 多年期山崩目錄建立之歷年山崩潛感模型結果 137
4.4 基礎山崩潛感圖 141
第五章 崩壞比分析 147
5.1 降雨量、潛感值與崩壞比關係曲線 148
5.1.1 各事件崩壞比擬合 149
5.2 山崩機率圖 177
第六章 討論 187
6.1 事件型山崩潛感模型比較 187
6.2 歷年山崩潛感模型與基礎山崩潛感圖比較 189
6.3 崩壞比擬合結果及山崩機率圖 190
第七章 結論與建議 191
7.1 結論 191
7.2 建議 192
參考文獻 193
附錄A 事件型山崩潛感模型交叉驗證潛感圖 196
附錄B 九期獨立事件不含岩性因子之基礎山崩潛感圖 233

李錫堤，潘國樑，林銘郎 (2003) 山崩調查與危險度評估-山崩潛感分析之研究(1/3)，經濟部中央地質調查所報告，第92-11號，154。
經濟部中央地質調查所 (2011) 易淹水地區上游集水區地質調查與資料庫建置‒集水區地質調查及山崩土石流調查與發生潛勢評估計畫，經濟部中央地質調查所，共700頁。
邱奕勛 (2012) 降雨量與誘發山崩發生率之關係，國立中央大學應用地質研究所碩士論文，共210頁。
張弼超 (2005) 運用羅吉斯迴歸法進行山崩潛感分析-以國姓地區為例，國立中央大學應用地質研究所碩士論文，共134頁。
張伯毅 (2013) 台灣山區之降與誘發山崩及山坡復育情形，國立中央大學應用地質研究所碩士論文，共377頁。
陳盈靜 (2014) 台灣山區降雨量與誘發山崩率之關係及集集大地震後山坡復育情形，國立中央大學應用地質研究所碩士論文，共193頁。
蔡雨澄 (2012) 極端降雨下之山崩潛感分析-以莫拉克颱風誘發山崩為例，國立中央大學應用地質研究所碩士論文，共126頁。
蔡心蘭 (2016) 台灣各子集水區之降雨與誘發山崩率之關係，國立中央大學應用地質研究所碩士論文，共157頁。
鍾意晴 (2008) 區域性山崩潛感分析探討-以石門水庫集水區為例，國立中央大學地球物理研究所碩士論文，共172頁。
簡逢助 (2015) 山崩潛感値暨降雨量與崩壞比之關係探討，國立中央大學應用地質研究所碩士論文，共151頁。
Agresti, A. (2002) Categorical data analysis (2nd ed.), John Wiley, New York, 710p.
Atkinson, P. M., Massari, R., (1998) Generalized linear modelling of susceptibility to landsliding in the central Apennines, Italy, Computers & Geosciences, 24, 373-385.
Burton, A., Bathurst, J. C., (1998) Physically based modeling of shallow　landslide sediment yield at a catchment scale, Environmental　Geology, 35, 89-99.
Budimir, M. E. A., Atkinson, P. M., Lewis, H. G., (2015) A systematic review of landslide probability mapping using logistic regression, Landslide, 12, 419-436.
Chang, K. T., Chiang, S. H., Hsu, M. L., (2007) Modeling typhoon- and earthquake-induced landslides in a mountainous watershed using logistic regression, Geomorphology, 89, 335-347.
Chang, K. T., Chiang, S. H., Lei, F., (2008) Analysing the relationship between typhoon-triggered landslides and critical rainfall conditions, Earth Surface Processes and Landforms, 33, 1261-1271.
Chung, C. F., Fabbri, A. G., (1999) Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering & Remote Seneing, 65, 12, 1389-1399.
Chung, C. F., Fabbri, A. G., (2003) Validation of spatial prediction models for　landslide hazard mapping, Natural Hazards, 30, 451-472.
Dai, F. C., Lee, C. F., (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong, Geomorphology, 42, 213-228.
Dai, F. C., Lee, C. F., Tham, L. G., Ng, K. C., Shum, W. L., (2004) Logistic regression modelling of storm-induced shallow landsliding in time and space on natural terrain of Lantau Island, Hong Kong, Bulletin of Engineering Geology and the Environment, 63, 315-327.
Feinberg, S. (1985) The Analysis of Cross-Classified Categorical Data (2nd ed.), Cambridge, MA, MIT Press, 198p.
Finlay, P. F., Fell, R., Maguire, P. K., (1997) The relationship between the probability of landslide occurrence and rainfall, Canadian Geotechnical Journal, 34, 811-824.
Jibson, R. W., Harp, E. L., Michael, J. A., (2000) A method for producing digital probabilistic seismic landslide hazard maps, Engineering Geology, 58, 271-289.
Lee, C. T., Pan, K. L., Lin, M. L., (2004) Research of landslide susceptibility analyses, Taiwan Central Geological Survey Open-File Report, 94-18, 474p.
Lee, C. T., Huang, C. C., Lee, J. F., Pan, K. L., Lin, M. L., Dong, J. J., (2008) Statistical approach to storm event-induced landslide susceptibility, Natural Hazard and Earth System Sciences, 8, 941-960.
Lee, C. T., (2014a) Multi-stage statistical landslide hazard analysis: rainfall-induced landslides, Landslide Science for a Safer Geoenvironment, 3, 291-298.
Lee, C. T., (2014b) Multi-stage statistical landslide hazard analysis: earthquake-induced landslides, Landslide Science for a Safer Gerenvironment, 3, 205-211.
Lee, C. T., Chung, C. C., (2017) Common patterns among different landslide susceptibility models of the same region, Advancing Culture of Living with Landslides, 2, 937-942.
Lillesand, T. M., Kiefer, R. W. (2000) Remote sensing and image interpretation, Wiley & Sons, New York, 724p.
Long, J. S. (1997) Regression models for categorical and limited dependent variables, Thousand Oaks, California: Sage Publications, 297p.
Ohlmacher, G. C., Davis, J. C., (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA, Engineering Geology, 69, 331-343.
Wilson, J. P., Gallant, J. C. (2000) Terrain analysis, John Wiley & Sons, Inc., 51-58.