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

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傳創(Truong-Tran Hoai Hieu) 查詢紙本館藏

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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)

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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

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指導教授

李錫堤(Chyi-Tyi Lee)

審核日期

2019-1-22

推文