博碩士論文 105624609 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:9 、訪客IP:54.221.145.174
姓名 傳創(Truong-Tran Hoai Hieu)  查詢紙本館藏   畢業系所 應用地質研究所
論文名稱 雨量誘發山崩潛感分析及驗證-以台灣曾文水庫集水區為例
(Rainfall Triggered Landslide Susceptibility Analysis and Validation in the Zengwen Reservoir Catchment, Taiwan)
相關論文
★ 台灣中部德基至梨山地區岩石劈理位態分布特性之研究★ 台北盆地松山層土壤性質之空間分析
★ 新店溪之地形研究★ 運用類神經網路進行隧道岩體分類
★ 大肚溪流域河階地形研究★ 台南台地暨鄰近地區之台南層及其構造運動
★ 台灣東北部地區隱沒帶地震強地動衰減式之研究★ 運用類神經網路進行地震誘發山崩之潛感分析
★ 地形地質均質區劃分與山崩因子探討★ 由世界應力量測資料探討不同地體構造區的應力特性
★ 921集集地震造成之地表變形模式★ 運用模糊類神經網路進行山崩潛感分析—以台灣中部國姓地區為例
★ 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★ 運用羅吉斯迴歸法進行山崩潛感分析-以臺灣中部國姓地區為例
★ 台灣西南平原末次冰期以來之地層及構造運動★ 利用近年大規模地震的強震資料修正Newmark經驗式
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 有許多廣泛使用的方法可以建立山崩潛感圖,例如地貌測繪、山崩目錄分析、統計方法、物理模型以及人工智慧等等。統計方法經常被用來分析山崩與山崩潛感因子之間的關係式,其中以羅吉斯回歸模型最為廣泛使用,因為其具有較高之準確性及穩定性。本篇論文之研究目的是以梅姬颱風誘發之山崩目錄訓練模型,建立台灣曾文水庫集水區之山崩潛感模型。我們需要分析數值高程模型、地質背景資料以及降雨量資料以取得山崩潛感因子,其中降雨量可以由雨量站量測資料統計內插取得。本研究使用兩種雨量內插方法,分別為普通克利金法(Ordinary Kriging, OK)以及回歸克利金(Regression Kriging, RK),並依據不同降雨量內插方法及不同潛感因子數量分為四種山崩潛感模型,分析結果顯示誘發山崩與潛感因子之間的空間關係良好。潛感模型之間的差異顯示,使用回歸克利金法內插雨量所得到的模型比起普通克利金法較佳及穩定,有利於建立穩定的潛感模型。根據分析結果,最後選用RK_8山崩潛感模型建立曾文水庫集水區之山崩潛感圖,可用於預測未來在不同雨量事件下之淺層山崩。
摘要(英) There are many methods which are commonly used to construct a landslide susceptibility map, such as geomorphological mapping, analysis of inventories, statistical method, physically based models, and artificial intelligence method. The statistical method is widely used to fit the mathematical relationship between observed landslides and the factors related to the influence of slope failure. The logistic regression model is the most popular for its robusticity and high accuracy. The purpose of this study is to establish a landslide susceptibility model in Zengwen Reservoir Catchment, Taiwan using statistical modeling techniques. Megi typhoon triggered landslides was selected to train the susceptibility model. DEM, geological data, and rainfall data are analyzed to achieve causative factors, besides that, the rainfall triggers are able to interpolate by ordinary kriging (OK) method and by regression kriging (RK) method, respectively, to build four of different landslide susceptibility models. The results show the spatial relationship between landslide occurrences and the causative factors is good. The comparative differences between models indicate regression kriging is better and suitable for interpolation the rainfall triggers and it is the good point to build the stable model. The RK_8 model is used to produce landslide susceptibility map of the region and used for prediction of future shallow landslides under different rain events with good prediction rate.
關鍵字(中) ★ 山崩
★ 山崩潛感
★ 普通克利金法
★ 回歸克利金法
★ 事件型山崩潛感分析模型
★ 交叉驗證
關鍵字(英) ★ Landslide
★ Landslide susceptibility
★ Ordinary Kriging
★ Regression Kriging
★ Event-based landslide susceptibility model
★ Cross-validation
論文目次 摘要 i
Abstract ii
Acknowledgments iii
Table of Contents iv
List of Tables vii
List of Figures viii
CHAPTER 1: INTRODUCTION 1
1.1 Landslide 1
1.2 Landslide susceptibility mapping 1
1.3 Research objectives 2
1.4 Thesis outline 2
CHAPTER 2: LITERATURE REVIEW 4
2.1 Spatial interpolation for rainfall data 4
2.1.1 Conventional methods 4
2.1.2 Geostatistical interpolation methods 4
2.2 Landslide susceptibility analysis 5
2.2.1 Qualitative methods 5
2.2.2 Quantitative methods 6
2.3 Validation and evaluation 9
2.3.1 Classification error matrix 9
2.3.2 Receiver-operating characteristic (ROC) 10
2.3.3 Success rate and prediction rate curve 10
CHAPTER 3: METHODOLOGY 12
3.1 Work procedure 12
3.2 Logistic regression 14
3.3 Selection of factor 14
3.3.1 Distribution of landslide and non-landslide 19
3.3.2 Probability of failure 20
3.3.3 P-P plot 20
3.3.4 Success rate 21
3.3.5 Correlation coefficient 21
3.4 Spatial rainfall interpolation 21
3.4.1 Ordinary kriging (OK) 22
3.4.2 Regression kriging (RK) 23
3.5 Validation 24
3.5.1 Success and prediction rate processing 24
3.5.2 Cross validation 25
CHAPTER 4: STUDY AREA AND DATA 26
4.1 Study area 26
4.1.1 Location, terrain, land cover 26
4.1.2 Stratigraphic and structure 27
4.1.3 Climate 30
4.2 Data collection 30
4.2.1 Rainfall data and processing 31
4.2.2 Remote sensing 35
4.2.3 Non remote sensing 35
4.3 Landslide inventory 36
4.3.1 Create landslide inventory map 36
4.3.2 Verification 40
4.4 Processing of factor 41
4.5 Factor selection results 55
CHAPTER 5: RESULT 57
5.1 Sampling of data for training model 57
5.2 Landslide potential model 58
5.3 Success rate curve 67
CHAPTER 6: MODEL VALIDATION AND COMPARISION 69
6.1 Model validation 69
6.2 Comparison with other nine models 74
CHAPTER 7: DISCUSSION 77
CHAPTER 8: CONCLUSIONS 79
8.1 Conclusions 79
8.2 Suggestions 80
REFERENCE 82
參考文獻 Akgun, A. (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey, Landslides, 9(1), 93-106.
Aleotti, P., Chowdhury, R. (1999) Landslide hazard assessment: summary review and new perspectives, Bulletin of Engineering Geology the Environment, 58(1), 21-44.
Ayalew, L., Yamagishi, H. (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan, Geomorphology, 65(1-2), 15-31.
Barredo, J., Benavides, A., Hervás, J., van Westen, C.J. (2000) Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain, International Journal of Applied Earth Observation Geoinformation, 2(1), 9-23.
Bishop, T., McBratney, A. (2001) A comparison of prediction methods for the creation of field-extent soil property maps, Geoderma, 103(1-2), 149-160.
Brenning, A. (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation, Natural HazardsEarth System Science, 5(6), 853-862.
Can, T., Nefeslioglu, H.A., Gokceoglu, C., Sonmez, H., Duman, T.Y. (2005) Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyses, Geomorphology, 72(1-4), 250-271.
Cardinali, M., Reichenbach, P., Guzzetti, F., Ardizzone, F., Antonini, G., Galli, M., Cacciano, M., Castellani, M., Salvati, P. (2002) A geomorphological approach to the estimation of landslide hazards and risks in Umbria, Central Italy, Natural Hazards and Earth System Science, 2(1/2), 57-72.
Chacón, J., Irigaray, C., Fernandez, T., El Hamdouni, R. (2006) Engineering geology maps: landslides and geographical information systems, Bulletin of Engineering Geology the Environment, 65(4), 341-411.
Chiang, S.-H., Chang, K.-T. (2009) Application of radar data to modeling rainfall-induced landslides, Geomorphology, 103(3), 299-309.
Chung, C., Fabbri, A. (1999) Probabilistic prediction models for landslide hazard mapping, Natural Hazards Photogrammetric Engineering Remote Sensing, 65(12), 1389-1399.
Chung, C., Fabbri, A. (2003) Validation of spatial prediction models for landslide hazard mapping, Natural Hazards and Earth System Science, 30(3), 451-472.
Crosta, G.B., Frattini, P. (2008) Rainfall‐induced landslides and debris flows, Hydrological Processes: An International Journal, 22(4), 473-477.
Cruden, D.M. (1991) A simple definition of a landslide, Journal of Structural Geology, 43(1), 27-29.
Dahal, R.K., Hasegawa, S., Nonomura, A., Yamanaka, M., Masuda, T., Nishino, K. (2008) GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping, Environmental Geology, 54(2), 311-324.
Dai, F., Lee, C., Li, J., Xu, Z. (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong, Environmental Geology, 40(3), 381-391.
Dai, F., Lee, C., Ngai, Y. (2002) Landslide risk assessment and management: an overview, Engineering Geology, 64(1), 65-87.
Dhaene, J., Goovaerts, M. (1997) On the dependency of risks in the individual life model, Insurance: Mathematics Economics, 19(3), 243-253.
Eldeiry, A.A., Garcia, L.A. (2010) Comparison of ordinary kriging, regression kriging, and cokriging techniques to estimate soil salinity using LANDSAT images, Journal of Irrigation Drainage Engineering, 136(6), 355-364.
Ermini, L., Catani, F., Casagli, N. (2005) Artificial neural networks applied to landslide susceptibility assessment, Geomorphology, 66(1-4), 327-343.
Fu, C.-C. (2016) Event-based Landslide Susceptibility and Rainfall-induced Landslide Probability Prediction Model in the Zengwen, Master thesis, National Central University.
Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., Reichenbach, P. (2008) Comparing landslide inventory maps, Geomorphology, 94(3-4), 268-289.
Goovaerts, P. (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall, Journal of Hydrology, 228(1-2), 113-129.
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.
Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., Galli, M. (2006) Estimating the quality of landslide susceptibility models, Geomorphology, 81(1-2), 166-184.
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.
He, Y., Beighley, R.E. (2008) GIS‐based regional landslide susceptibility mapping: a case study in southern California, Earth Surface Processes Landforms, 33(3), 380-393.
Hosmer, D., Lemeshow, S. (2000) Applied Logistic Regression., 2nd edn. (Wiley: New York.), NY, USA.
Knotters, M., Brus, D., Voshaar, J.O. (1995) A comparison of kriging, co-kriging and kriging combined with regression for spatial interpolation of horizon depth with censored observations, Geoderma, 67(3-4), 227-246.
Lee, C.T. (2013). Re-evaluation of factors controlling landslides triggered by the 1999 Chi–Chi earthquake. In Earthquake-Induced Landslides (pp. 213-224): Springer.
Lee, C.T. (2014) Statistical seismic landslide hazard analysis: An example from Taiwan, Engineering Geology, 182, 201-212.
Lee, C.T. (2016) Empirical Relationship for Probability of Earthquake induced Landslide Failure. Paper presented at the EGU General Assembly Conference Abstracts.
Lee, C.T., Huang, C.C., Lee, C.F., Pan, K.L., Lin, M.L., Liao, C.W., Lin, P.S., Lin, Y.S., Chang, C.W. (2004) Landslide susceptibility analysis based on three different triggering events and result comparison. Paper presented at the Proceeding of International Symposium on Landslide and Debris Flow Hazard Assessment.
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 Earth System Sciences, 8(4), 941-960.
Lillesand, T.M., Kiefer, R.W., Chipman, J. (2000) Remote Sensing and Image Analysis, John Wiley Sons, New York.
Lu, P., Catani, F., Tofani, V., Casagli, N. (2014) Quantitative hazard and risk assessment for slow-moving landslides from Persistent Scatterer Interferometry, Landslides, 11(4), 685-696.
Ly, S., Charles, C., Degré, A. (2013) Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modeling at watershed scale: a review, Biotechnology, Agronomy, Society Environment, 17(2), 392-406.
Mair, A., Fares, A. (2010) Comparison of rainfall interpolation methods in a mountainous region of a tropical island, Journal of Hydrologic Engineering, 16(4), 371-383.
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(3-4), 331-343.
Pan, K.-L., Lee, C.-T., Chang, C.-W., Lin, Y.-H., Lin, S.-Y., Lee, J.-F., Wei, C.-Y., Liao, C.-W. (2004) Inventory of event-induced landslides by using space imagery. Paper presented at the Proceeding of International Symposium on Landslide and Debris Flow Hazard Assessment.
Park, N.W., Chi, K.H. (2008) Quantitative assessment of landslide susceptibility using high‐resolution remote sensing data and a generalized additive model, International Journal of Remote Sensing, 29(1), 247-264.
Pradhan, B., Lee, S. (2010a) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models, Environmental Earth Sciences, 60(5), 1037-1054.
Pradhan, B., Lee, S. (2010b) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling, Environmental Modelling Software, 25(6), 747-759.
Reichenbach, P., Rossi, M., Malamud, B., Mihir, M., Guzzetti, F. (2018) A review of statistically-based landslide susceptibility models, Earth-science reviews.
Remondo, J., González, A., De Terán, J.R.D., Cendrero, A., Fabbri, A., Chung, C.-J.F. (2003) Validation of landslide susceptibility maps; examples and applications from a case study in Northern Spain, Natural Hazards and Earth System Science, 30(3), 437-449.
Satellite Imaging, C. (2012) SPOT-6 Satellite Sensor (1.5m). Retrieved from https://www.satimagingcorp.com/satellite-sensors/spot-6/
Soeters, R., van Westen, C.J. (1996) Landslides: Investigation and mitigation. Chapter 8-Slope instability recognition, analysis, and zonation, Transportation research board special report(247).
Süzen, M.L., Doyuran, V. (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate, Environmental geology, 45(5), 665-679.
Thiery, Y., Malet, J.-P., Sterlacchini, S., Puissant, A., Maquaire, O. (2007) Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment, Geomorphology, 92(1-2), 38-59.
Varnes, D.J. (1978) Slope movement types and processes, Special Report, 176, 11-33.
Wang, H., Ren, L.L., Liu, G.H. (2009) A regression-kriging model for estimation of rainfall in the Laohahe basin. Paper presented at the International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining.
Webster, R., Oliver, M.A. (2001) Geostatistics for environmental scientists (Statistics in Practice), American Journal of Climate Change.
Wilson, J., Gallant, J. (2000). Digital terrain analysis. In Terrain Analysis: Principles and Applications (Vol. 6, pp. 1-27).
Yalcin, A. (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations, Catena, 72(1), 1-12.
Yalcin, A., Reis, S., Aydinoglu, A., Yomralioglu, T. (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey, Catena, 85(3), 274-287.
Yesilnacar, E., Topal, T. (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey), Engineering Geology, 79(3-4), 251-266.
Yilmaz, I. (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey), Computers Geosciences, 35(6), 1125-1138.
Zhu, Q., Lin, H. (2010) Comparing ordinary kriging and regression kriging for soil properties in contrasting landscapes, Pedosphere, 20(5), 594-606.
指導教授 李錫堤(Chyi-Tyi Lee) 審核日期 2019-1-22
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明