Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/97663
標題: 結合多年期崩塌目錄及降雨因子建立崩塌機率模型
Landslide Probability Model Combined with Long-term Landslide Inventories and Rainfall Factor
作者: 葉彥駒
Yen-Chu Yeh
關鍵字: 崩塌;分析單元;降雨門檻;崩塌機率模型;landslide;mapping unit;rainfall threshold;landslide probability model
引用: 1. 吳俊毅、蔡喬文、陳樹群(2016),高屏溪流域崩塌地之地形特徵分析。中華水土保持學報,47(3),156-164。 2. 林慶偉、林美聆、張中白、吳銘志、王泰典、陳天健(2010),莫拉克颱風受災區域之地質敏感特性分析 (1/3)。經濟部中央地質調查所。 3. 符智傑(2016),曾文水庫集水區事件型降雨誘發山崩潛感及山崩機率預測模式。國立中央大學應用地質研究所碩士論文。 4. 莊緯璉(2005),運用判別分析進行山崩潛感分析之研究-以臺灣中部國姓地區為例。國立中央大學應用地質研究所碩士論文。 5. 經濟部中央地質調查所,2013,都會區周緣坡地山崩潛勢評估。財團法人中興工程顧問社。 6. 詹錢登(2003),以降雨因子進行土石流警戒基準值訂定。行政院農委會水土保持局委託計畫成果報告書。 7. 劉進金、翁勳政、黃金鴻、楊明宗(2001),豪雨型崩塌地之遙測影像分析。21世紀土木工程技術與管理研討會論文集,新竹,第C-21~C-31 頁。 8. 蔡明璋(2016),應用雙因子存活分析於建立土石流預警臨界曲線之研究—以台灣神木地區為例。逢甲大學土木及水利工程博士學位學程博士論文。 9. 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。 10. 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。 11. 簡逢助(2015),山崩潛感值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。 12. Afungang, R. N., & Bateira, C. V. (2016). Temporal probability analysis of landslides triggered by intense rainfall in the Bamenda Mountain Region, Cameroon. Environmental Earth Sciences, 75(12), 1032. 13. Baeza, C., & Corominas, J. (2001). Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 26(12), 1251-1263. 14. Bernknopf, R. L., Campbell, R. H., Brookshire, D. S., & Shapiro, C. D. (1988). A probabilistic approach to landslide hazard mapping in Cincinnati, Ohio, with applications for economic evaluation. Bulletin of the Association of Engineering Geologists, 25(1), 39-56. 15. Bonham-Carter, G. F. (2014). Geographic information systems for geoscientists: modelling with GIS (Vol. 13). Elsevier. 16. Bonham-Carter, G. F. (1989). Weights of evidence modeling: a new approach to mapping mineral potential. Statistical applications in the earth sciences, 171-183. 17. Bründl, M., Romang, H. E., Bischof, N., & Rheinberger, C. M. (2009). The risk concept and its application in natural hazard risk management in Switzerland. Natural Hazards and Earth System Sciences, 9(3), 801-813. 18. Cardinali, M., Carrara, A., Guzzetti, F., & Reichenbach, P. (2002). Landslide hazard map for the Upper Tiber River basin. CNR, Gruppo Nazionale per la Difesa dalle Catastrofi Idrogeologiche, Publication, (2116). 19. Cardinali, M., Galli, M., Guzzetti, F., Ardizzone, F., Reichenbach, P., & Bartoccini, P. (2006). Rainfall induced landslides in December 2004 in south-western Umbria, central Italy: types, extent, damage and risk assessment. Natural Hazards and Earth System Science, 6(2), 237-260. 20. Carrara, A. (1983). Multivariate models for landslide hazard evaluation. Journal of the International Association for Mathematical Geology, 15(3), 403-426. 21. Carrara, A. (1988). Drainage and divide networks derived from high-fidelity digital terrain models. In Quantitative analysis of mineral and energy resources (pp. 581-597). Springer, Dordrecht. 22. Carrara, A., Cardinali, M., Guzzetti, F., & Reichenbach, P. (1995). GIS technology in mapping landslide hazard. In Geographical information systems in assessing natural hazards (pp. 135-175). Springer, Dordrecht. 23. Carrara, A., Crosta, G., & Frattini, P. (2008). Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology, 94(3-4), 353-378. 24. Chung, C. J. F., & Fabbri, A. G. (1999). Probabilistic prediction models for landslide hazard mapping. Photogrammetric engineering and remote sensing, 65(12), 1389-1399. 25. Chung, C. J. F., Fabbri, A. G., & Van Westen, C. J. (1995). Multivariate regression analysis for landslide hazard zonation. In Geographical information systems in assessing natural hazards(pp. 107-133). Springer, Dordrecht. 26. Chung, C. J. F., & Fabbri, A. G. (1993). The representation of geoscience information for data integration. Nonrenewable Resources, 2(2), 122-139. 27. Clerici, A., Perego, S., Tellini, C., & Vescovi, P. (2002). A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology, 48(4), 349-364. 28. Corominas, J., van Westen, C., Frattini, P., Cascini, L., Malet, J. P., Fotopoulou, S., ... & Pitilakis, K. (2014). Recommendations for the quantitative analysis of landslide risk. Bulletin of engineering geology and the environment, 73(2), 209-263. 29. Crovelli, R. A. (2000). Probability models for estimation of number and costs of landslides. Reston, VA: US Geological Survey. 30. Davis, J. C. (2002) Statistics and Data Analysis in Geology. 3rd edition, 638, Wiley. 31. De Vita, P. (2000). Fenomeni di instabilità delle coperture piroclastiche dei Monti Lattari, di Sarno e di Salerno (Campania) ed analisi degli eventi pluviometrici determinanti. Quaderni di Geologia Applicata, 7(2), 213-235. 32. Erener, A., & Düzgün, H. S. (2013). A regional scale quantitative risk assessment for landslides: case of Kumluca watershed in Bartin, Turkey. Landslides, 10(1), 55-73. 33. Ghosh, S., van Westen, C. J., Carranza, E. J. M., & Jetten, V. G. (2012). Integrating spatial, temporal, and magnitude probabilities for medium-scale landslide risk analysis in Darjeeling Himalayas, India. Landslides, 9(3), 371-384. 34. Ghosh, S., van Westen, C. J., Carranza, E. J. M., Jetten, V. G., Cardinali, M., Rossi, M., & Guzzetti, F. (2012). Generating event-based landslide maps in a data-scarce Himalayan environment for estimating temporal and magnitude probabilities. Engineering geology, 128, 49-62. 35. Guzzetti, F. (2006) Landslide Hazard and Risk Assessment. PhD Thesis, Universitäts-und Landesbibliothek Bonn. 36. Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1), 181-216. 37. Guzzetti, F., Galli, M., Reichenbach, P., Ardizzone, F., & Cardinali, M. (2006). Landslide hazard assessment in the Collazzone area, Umbria, Central Italy. Natural Hazards and Earth System Science, 6(1), 115-131. 38. Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., & Ardizzone, F. (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology, 72(1-4), 272-299. 39. Guzzetti, F., Reichenbach, P., Cardinali, M., Ardizzone, F., & Galli, M. (2003). The impact of landslides in the Umbria region, central Italy. Natural Hazards and Earth System Science, 3(5), 469-486. 40. Hansen, M. J. (1984). Strategies for classification of landslides. Slope instability, 1-25. 41. Jaiswal, P., & van Westen, C. J. (2009). Estimating temporal probability for landslide initiation along transportation routes based on rainfall thresholds. Geomorphology, 112(1-2), 96-105. 42. Lee, C. T., & Chung, C. C. (2017, May). Common patterns among different landslide susceptibility models of the same region. In Workshop on World Landslide Forum (pp. 937-942). Springer, Cham. 43. Lee, C. T. (2014). Multi-stage statistical landslide hazard analysis: earthquake-induced landslides. In Landslide science for a safer geoenvironment (pp. 205-211). Springer, Cham. 44. 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 landslides susceptibility. Natural hazards and earth system sciences, 8(4), 941-960. 45. Lee, S. (2004). Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS. Environmental Management, 34(2), 223-232. 46. Luckman, P. G., Gibson, R. D., & Derose, R. C. (1999). Landslide erosion risk to New Zealand pastoral steeplands productivity. Land Degradation & Development, 10(1), 49-65. 47. Moore, I. D., & Grayson, R. B. (1991). Terrain‐based catchment partitioning and runoff prediction using vector elevation data. Water Resources Research, 27(6), 1177-1191. 48. Moore, I. D., O'loughlin, E. M., & Burch, G. J. (1988). A contour‐based topographic model for hydrological and ecological applications. Earth Surface Processes and Landforms, 13(4), 305-320. 49. Nefeslioglu, H. A., & Gokceoglu, C. (2011). Probabilistic risk assessment in medium scale for rainfall-induced earthflows: Catakli catchment area (Cayeli, Rize, Turkey). Mathematical Problems in Engineering, 2011. 50. O'loughlin, E. M. (1986). Prediction of surface saturation zones in natural catchments by topographic analysis. Water Resources Research, 22(5), 794-804. 51. Önöz, B., & Bayazit, M. (2001). Effect of the occurrence process of the peaks over threshold on the flood estimates. Journal of hydrology, 244(1-2), 86-96. 52. Pasuto, A., & Silvano, S. (1998). Rainfall as a trigger of shallow mass movements. A case study in the Dolomites, Italy. Environmental Geology, 35(2-3), 184-189. 53. Pike, R. J. (1988). The geometric signature: quantifying landslide-terrain types from digital elevation models. Mathematical geology, 20(5), 491-511. 54. Rossi, M., Guzzetti, F., Reichenbach, P., Mondini, A. C., & Peruccacci, S. (2010). Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology, 114(3), 129-142. 55. Van Den Eeckhaut, M., Reichenbach, P., Guzzetti, F., Rossi, M., & Poesen, J. (2009). Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium. Natural Hazards and Earth System Sciences, 9(2), 507-521. 56. Van Westen, C. J. (1993). Application of geographic information systems to landslide hazard zonation. 57. Van Westen, C. J. (1994). GIS in landslide hazard zonation: a review, with examples from the Andes of Colombia. In Mountain environments & geographic information systems. Taylor & Francis. 58. Van Westen, C. J., Rengers, N., Terlien, M. T. J., & Soeters, R. (1997). Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation. Geologische Rundschau, 86(2), 404-414. 59. Varnes, D. J., & IAEG (1984). Landslide hazard zonation: a review of principles and practice (No. 3). 60. Varnes, D. J. (1978). Slope movement types and processes. Special report, 176, 11-33. 61. Vasu, N. N., Lee, S. R., Pradhan, A. M. S., Kim, Y. T., Kang, S. H., & Lee, D. H. (2016). A new approach to temporal modelling for landslide hazard assessment using an extreme rainfall induced-landslide index. Engineering Geology, 215, 36-49. 62. Wu, C. Y., & Chen, S. C. (2013). Integrating spatial, temporal, and size probabilities for the annual landslide hazard maps in the Shihmen watershed, Taiwan. Natural Hazards and Earth System Sciences, 13(9), 2353-2367. 63. Wu, Y., Chen, L., Cheng, C., Yin, K., & Török, Á. (2014). GIS-based landslide hazard predicting system and its real-time test during a typhoon, Zhejiang Province, Southeast China. Engineering Geology, 175, 9-21. 64. Xie, M., Esaki, T., & Zhou, G. (2004). GIS-based probabilistic mapping of landslide hazard using a three-dimensional deterministic model. Natural Hazards, 33(2), 265-282. 65. Zêzere, J. L., Oliveira, S. C., Garcia, R. A. C., & Reis, E. (2007). Landslide risk analysis in the area North of Lisbon (Portugal): evaluation of direct and indirect costs resulting from a motorway disruption by slope movements. Landslides, 4(2), 123-136.
摘要: 
崩塌災害具有相當之不確定性,尤其是在大範圍的研究區域常不易得知崩塌將會發生的位置,因此越來越多研究以機率模式分析崩塌發生之機率。
本研究以臺北水源特定區為研究對象,並選以2000~2015年之8次崩塌事件作分析。首先探討適合本研究之分析單元,得到分析單元之選擇需考量崩塌地面積以及呈現機率分布時之情形。本研究考量研究區面積較大,因此選擇邊坡單元作為分析單元,且其較能呈現邊坡之地文特性。再來根據雨量站使用徐昇氏多邊形法劃分研究區,以得到共7個雨量站控制區域。
另一方面,本研究蒐集研究區內雨量站之日雨量資料並計算其有效累積雨量,用以建立各雨量站之聯合累積分配函數,再而設立機率門檻,以計算超過門檻之降雨次數,再用Poisson機率模型超過門檻雨型組未來一年內再發生之機率,同時也計算在超過門檻降雨發生下崩塌之機率。最後將兩者機率值相乘,即可得到未來一年發生崩塌之機率。
本研究中得到研究區內崩塌機率較高處分布在研究區西南方之福山(3)雨量站控制區,機率值最高之單元為0.15100,除了因為該區域之岩性較脆弱外,也反映與該地區較高之高程及較陡之坡度有關。另一方面,本研究所建立結合雨量之崩塌機率模型較僅以崩塌目錄做分析之機率模型相比,優點為:1.能反映不同雨量站控制區之崩塌機率差異。2.雨量資料統計年數較崩塌目錄長、較為可靠。3.若事先知道降雨機率之變化,能應其變化而調整原本之模型。

Landslide hazard has considerably uncertainty. Especially in a wide region, it is not easy to confirm the exact area that landslides may happen. Thus more and more researches use probability models to study landslide probabilities.
In this study, the research area is Taipei Water Source Domain, and we choose 8 landslide events from 2000~2015 for analysis. First, we discuss the appropriate mapping unit, and obtained that it is needed to consider the landslide area and the result of probability distribution. In this study, we considered that study area has wide extent, thus we choose slope units as mapping unit, and these can present the geophysical characteristics of a slope. Then we used Thiessen polygons to divide study area into 7 sections.
On the other hand, we collect daily rainfall from rainfall station in study area, and use it to calculated effective accumulated precipitation. Then we calculated the joint cumulative distribution function for each rainfall station. And next we established probability threshold to count how many times that rainfall exceeded the threshold. Then we used Poisson probability model to calculate the probability of a rainfall event that exceeded the threshold in one year. At the same time, we calculated the probability of landslide under this rainfall condition. Finally, we multiplied these two probability values and obtained the landslide probability in one year.
In this study, we obtained that mapping units with higher probability distributed in the southwest of study area, the Fushan(3) rainfall station control area, and the mapping unit with the highest probability value is 0.15100. Except for the reason of weak lithology, it also reveal the relationship with higher elevation and steeper slope. On the other hand, comparison with the landslide probability model just establish with landslide inventories, the landslide probability model combined with rainfall factor shows advantages of: 1.Reflecting the different landslide probability of different rainfall station control area. 2.Rainfall data has longer statistical year than landslide inventories, so it is more reliable. 3.If knowing the change of rainfall probability, we can adjust the original model based on the change of it.
URI: http://hdl.handle.net/11455/97663
Rights: 同意授權瀏覽/列印電子全文服務,2019-08-31起公開。
Appears in Collections:水土保持學系

Files in This Item:
File SizeFormat Existing users please Login
nchu-107-7105042207-1.pdf3.8 MBAdobe PDFThis file is only available in the university internal network   
Show full item record
 
TAIR Related Article

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.