博碩士論文 105022606 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:8 、訪客IP:34.229.126.29
姓名 李曼諾(Emmanuel Leonard)  查詢紙本館藏   畢業系所 遙測科技碩士學位學程
論文名稱 利用人工神經網絡模型建立多事件為基礎之崩塌模型-以台灣玉山國家公園為例
(Multiple Event-based Landslide Modeling Using Artificial Neural Network Model: A Case Study in the Yushan National Park, Taiwan)
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摘要(中) 崩塌現象一直是世界上最嚴重的自然災害之一。崩塌災害的分析主要是藉由分析邊坡的不穩定因素來進行。近年來,利用地理資訊系統(GIS)為常見的分析工具,可將各類環境圖層進行整合、分析以及圖資產製。一般來說,邊坡危險性的評估通常可通過分析由不同的觸發誘因(包括暴雨,地震和人類活動)導致的歷史崩塌事件來進行。然而,在許多地區,崩塌的出現可能並不為由單一的事件來觸發,這導致基於單一崩塌事件資料來建立的崩塌模式可能有相當程度的不確定性和局限性。近年來,人工神經網絡(ANN)為常用來進行崩塌模式的方法,其優勢為可以考量邊坡穩定性和崩塌環境因素之間可能存在的非線性的關係。相較於其他利用單一事資料進行崩塌模式建立的研究、抑或是利用線性的模式方法,件本研究則嘗試利用ANN方法建立多事件為基礎的崩塌模型,以因應崩塌現象的複雜性。本研究選取位於台灣中部的玉山國家公園作為研究區,該地區地質、地形條件複雜,加上過去曾遭受不同規模颱風的多次侵襲,本研究認為該區的崩塌環境複雜,為適合的試驗地點。本研究收集了1996年賀伯颱風,2001桃芝颱風年和2009年莫拉克颱風三場歷史颱風事件,並利用人工神經網絡針對研究區建立了多事件為基礎的崩塌模型。模式建立使用的因子包括:地形高程、坡度,坡向,地形曲率,地形濕度指數,距斷層距離以及事件前正規化植生指數 (Normalized Difference Vegetation Index, NDVI) ,以及事件之總降雨量,降雨強度,降雨延時等。具體來說,本研究設計了兩個試驗來測試人工神經網絡模型的適用性:(1)基於單一事件資料進行模型建構(Single Event-based Modeling, SEB):使用單一颱風事件的資料建構崩塌模型,並以另外兩個颱風事件資料行模型驗證; (2)基於多事件資料進行模型建構(Multiple Event-based Modeling, MEB ):利用兩個颱風事件的資料建立一個崩塌模型,並用另一個事件進行模型驗證。另外,本研究以同時將ANN模型與Chang and Chiang(2009)提出的線性模式—整合式模型(Integrated Model, IM)進行比較。
本研究使用ROC 曲線 (Receiver Operating Characteristic) 來進行模式評估, ROC曲線下面積—AUC(area under ROC curve)作為評量模型的量化指標。研究結果顯示,MEB模型比SEB模型表現更好:ANN部分,ANN-SEB模型的AUC為68% - 88%,而ANN-MEB模型為87% - 91%;IM方面,IM-SEB模型的AUC為65% - 67%,IM-MEB為75% - 90%。平均而言,模式驗證之AUC為:ANN-SEB為78%、ANN-MEB為89%,相對於IM-SEB為66%、IM-MEB為84% 較高。研究成果顯示了利用多事件資料來建構崩塌模型的重要性,並驗證了非線性的ANN方法在玉山國家公園崩塌災害評估中的適用性。最後,本研究成果可以作為玉山國家公園針對邊坡災害進行相關經營管理對策之參考。
摘要(英) Landslide phenomenon continues to be one of the worst natural disasters around the world. Landslide hazard mapping is usually performed through the identification and analysis of hillslope instability factors, recently managed as thematic data within geographic information systems (GIS) environment. Therefore, landslide hazard assessment is normally conducted by analyzing historical landslide events which can be caused by different triggers, including heavy rainfall, earthquake and human activities. In many regions, however, the presence of landslides is not subject to single trigger or event, leading to uncertainties and limitations of single event-based landslide analysis. In recent years, Artificial Neural Network (ANN) is considered as one of the commonly used not only because it can deal with complex and non-linear relationships between slope stability and conditioning factors to predict landslide but also minimize subjectivity. Different from previous studies which mainly focuse on single event-based landslide analysis, for landslide modeling, this study examines multiple landslide events using an ANN approach. Yushan National Park (YNP), located in central Taiwan, was chosen as an ideal test site, as it is a good representative of many geoenvironmental settings within this region and has been continuously affected by typhoons with different magnitude, creating a complex landslide environment. In this research three typhoons that struck Taiwan in 1996, 2001 and 2009 were collected to develop the multiple event-based landslide model using ANN. To develop the ANN model, several landslide occurrence factors were applied including: elevation, slope, aspect, curvature, topographic wetness index, distance to fault, pre-event Normalized Difference Vegetation Index (NDVI), and rainfall variables such as, total rainfall, rainfall intensity, rainfall duration.
Specifically, two experiments were designed to test the applicability of ANN model: (1) single event-based modeling (SEB): developing a landslide model using data from one typhoon event, and validate the model with other two typhoon events; (2) multiple event-based modeling (MEB): developing a landslide model using data from two events and validate the model with the other event. In addition, the ANN model was compared with a linear event-based landslide model, the integrated model (IM), proposed by Chang and Chiang (2009).
To evaluate the model performance, this study used the Receiver Operating Characteristic (ROC) curve, which is based on the proportions of incidences correctly reported as positive (true positive) and incidences erroneously reported as positive (false positive). The area under the ROC curve (AUC) then measures the fitness of the model: the larger the area, the better the model.
The results show multiple event-based models perform better than single ones. Meanwhile, the ANN generally performs better than IM. For ANN-SEB, the validation accuracies vary from 68% – 88% and 87% – 91% for ANN-MEB. For IM-SEB, the validation accuracies vary from 65% – 67% and 75% – 90% for IM-MEB. In average, the validation set accuracies are: 78% for ANN-SEB and 89% for ANN-MEB; 66% for IM-SEB and 84% for IM-MEB. This study demonstrates the importance of considering multiple events in landslide modeling, and also reveals the applicability of ANN method in landslide hazard assessment for Yushan National Park. Finally, this work can be used as a reference to assist slope failure, slope management and tourism planning considering landslide hazard in Yushan National Park.
關鍵字(中) ★ 邊坡危險性
★ 多事件資料
★ 人工神經網絡
★ 線性模式—整合式模型
★ 玉山國家公園
關鍵字(英) ★ Landslide hazard assessment
★ Multiple Event-Based
★ ANN
★ Integrated Model
★ Yushan National Park
論文目次 ABSTRACT iv
中文摘要 vi
ACKNOWLEDGMENT viii
LIST OF FIGURES xii
LIST OF TABLES xiv
LIST OF ABBREVIATIONS AND ACRONYMS xv
CHAPTER 1 – INTRODUCTION 1
1.1.- Research background 1
1.2.- Statement of the problem 2
1.3.- Research questions and objectives 4
1.4.- Brief description of the methods applied 4
(1) Single event-based modeling (SEB) 4
(2) Multiple event-based modeling (MEB) 4
1.5.- Structure of the thesis 5
CHAPTER 2 – LITERATURE REVIEW 6
2.1.- Landslide investigations 6
2.1.1.- Landslide recognition 6
2.1.2.- Landslide monitoring 7
2.1.3.- Landslide hazard analysis and prediction 7
2.2.- Landslide hazard assessment and ANN 9
2.3.- Single event-based and multiple event-based landslide hazard assessment 11
CHAPTER 3 – STUDY AREA AND TYPHOON EVENTS 12
3.1.- Study area 12
3.2.- Typhoon events 15
3.2.1.- Typhoon Herb 15
3.2.1.- Typhoon Toraji 15
3..2.3.- Typhoon Morakot 16
CHAPTER 4 – DATA USED 17
4.1. - Data acquisition 17
4.2. – Geo-spatial database preparation 17
4.2.1. - Topographic parameters 18
4.2.1.1- Elevation 18
4.2.1.2.- Slope gradient 21
4.2.1.3.- Aspect or slope exposure 22
4.2.1.4.- Curvature 24
4.2.1.5.- Topographic Wetness Index (TWI) 25
4.2.2.- Lithology 27
4.2.3.- Distance to fault line 29
4.2.4.- Vegetation index (NDVI) 30
4.2.5.- Rainfall 35
4.2.6.- Landslide Inventory Map (LIM) 44
CHAPTER 5 – METHODOLOGY 48
5.1.- Study framework 48
5.2.- Artificial Neural Network Model 48
5.3.- Landslide Modeling using ANN 51
5.4. Assessing prediction performance and validation of the probability maps 54
5.5.- Cutoff-independent performance criteria 55
56
5.6.- Models validation 56
5.7.- Effect analysis 56
5.8.- Landslide Modeling using an Integrated Model (IM) 57
5.8.1.- Critical rainfall model and input parameters 58
5.8.2.- Logistic regression 59
CHAPTER 6 – RESULTS 61
6.1.- Artificial Neural Network model (ANN) 61
6.1.1.- Single Event-Based approach (ANN-SEB) 61
6.1.2.- Multiple Event-Based approach (ANN-MEB) 63
6.1.3.- ANN Model validation 64
6.2.- Integrated Model (IM) 68
6.2.1.- Single event-based (IM-SEB) 68
6.2.2.- Multiple event-based (IM-MEB) 68
CHAPTER 7 – DISCUSSIONS 70
7.1.- Artificial Neural Network model 70
7.2.- Integrated Model 71
7.3.- ANN versus IM 71
7.4.- Single Event-Based versus Multiple Event-Based approach 71
7.5.- Effect analysis 72
7.6.- Limitations 73
CHAPTER 8 – CONCLUSION 76
REFERENCES 77
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指導教授 姜壽浩(Shou-Hao Chiang) 審核日期 2018-9-21
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