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Coupling TRIGRS and TOPMODEL
in shallow landslide Prediction
報告者:李浩瑋
指導教授:李錫堤
2011.5.19
Out line
Introduction
Literature review
Objective
Methodology
Result and discussion
Conclusion
2
Landslide Susceptibility Analysis
Qualitative analysis:
 Empirical method
Quantitative analysis:
Statistic method
-based upon long-period landslide inventories
Deterministic analysis
-strength parameters,
-failure depth
-groundwater conditions
3
Literature review
1) By combining an infinite-slope stability calculation with a transient,
one-dimensional analytic solution for pore pressure response to
transient rainfall infiltration.[Iverson, 2000; Baum et al., 2002;
Savage et al., 2003; Godt, 2004]
2) TRIGRS models were used for slope stability analysis. [吳佳郡,
2006;王姵兮,2007;鐘欣翰,2008]
1) Concept of topographic index, ln(a/tanβ).[Beven and Kirby, 1979]
2) A hydrological simulation based on a modified version of
TOPMODEL was developed to estimate the temporal
groundwater level for conducting the slope-instability analysis.
[李光敦, 2009]
4
Objective
rainfall-triggered shallow landslide
1) Consider the lateral flow
2) Compare the result of trigrs in shallow landslide
prediction
5
Research Process
Start
Precipitation
T=0, Input initial groundwater table
Hydrological model
KZ、D0、IZ
TRIGRS (1-D infiltration)
Simulating
groundwater table
Infinite slope
model
Prediction of
shallow landslide
T=t+dt
TOPMODEL
(Modification of water table)
T=final t
End
6
TRIGRS
TRIGRS (Transient Rainfall Infiltration
stability Model (Baum et al., 2002)
Grid-based Regional Slope-
Infiltration model
(Iverson, 2000)
D0  2

( 2 ) 2
t
cos  Z
D0 = Ksat/C0
D0 =飽和水力擴散率
Ksat = 飽和水力傳導係數
C0 =土壤最小之含水量
Z= z/cos α
Conceptual sketch of the hydrological model
Z =鉛直方向
in TRIGRS(after Godt 2004)
z =垂直坡面方向
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TOPMODEL
TOPMODEL(TOPgraphy based hydrological MODEL)(Beven.
et al., 1979)
a: Specific area
tan β :slope
 a 

TI  ln 
 tan  
a
Z j  Z  m[  ln(
)j]
tan
Zj
Zj :Depth to groundwater table
Z: Average depth of the groundwater table
m : Recession constant
λ : mean value of the Topographic index
8
Infinite slope
文獻回顧
resistance force C  ( s D   w hw ) cos 2  tan 
FS 

driving force
 s D sin  cos 
FS 
FS>1 Stable
FS<1 Unstable
 h tan 
C
 [1  w ( w )]
 s D sin  cos 
 s D tan 
未來工作
TOPMODEL
Calculate Zj
 w D  Z j tan 
C
FS 
 [1  (
)]
 s D sin  cos 
s
D
tan 
Schematic diagrams of the coupled hydrologicalslope instability model(after Lee 2009)
9
Study area
文獻回顧
10
Event Analysis
Event-based Triggered Landslide Inventory
Typhoon Aere Rainfall Records
2004/8/23~8/25
GAOYI Station
11
hydrological parameters
鍾(2008)
地質區
Kz(m/s)
D0(m2/s)
Iz(m/s)
zone1
9×10-4
1.8×10-3
10-8
zone2
10-3
2×10-3
10-8
zone3
2×10-3
4×10-3
10-8
12
Event Analysis
Antecedent rainfall
2004/8/20
Initial water table
2004/8/22
Typhoon Aere
13
Groundwater level simulation
Rainfall period
14
Geologic parameters
鍾(2008)
地質區
γs(kN/m2)
Cmax(kPa)
ψ(°)
zone1
18.2
8
36
zone2
19.6
9
28
zone3
18.9
9
27
15
Model result
Rainfall period
16
Model
validation
Model validate
• Error matrix (Stehman, 1997)
success curve and prediction
curve (Chung and Fabbri, 1999)
全區資料
網格數
Unstable
(FS < 1)
Stable
(FS ≧1)
Unstable
N1
N2
stable
N3
N4
總體正確率=(N1+N2)/(N1+N2+N3+N4)
Success rate
分類結果網格數
Area in percentage
Different rainfall situations for validation
─Using Typhoon Masta Rainfall(2005)
Result
Success Rate Curve
Predict
121809
Actual
Unstable
(FS < 1)
Stable
(FS ≧1)
Unstable
625
446
stable
11530
108488
山崩組正確率58.35%、非山崩組正確率89.59%,總體正確率89.10%
18
Model validation
Typhoon
Aere
Typhoon Masta
Prediction
Success Rate Curve
Typhoon
Typhoon Aere
Mastalandslide
landslidemap
map
19
Discussion
Success Rate Curve
TRIGRS
Success Rate Curve
Coupling TRIGRS and TOPMODEL
TRIGRS
結合TRIGRS
與TOPMODEL
山崩組正確率
56.7%
58.4%
非山崩組正確率
87.4%
89.5%
總正確率
87.2%
89.1%
20
Discussion
(C)
(12)
(11) (10)
(A) (1)
(9)
(2)
(8)
(7)
(3)
(B)
(6)
(4)
(5)
Coupling TRIGRS and TOPMODEL與TRIGRS分析結果套疊圖
21
Discussion
集水區邊界
水系
艾利颱風崩塌地
Coupling TOPMODEL < 1
TRIGRS ≧ 1
Coupling TOPMODEL ≧ 1
TRIGRS < 1
Coupling TOPMODEL < 1
TRIGRS < 1
Coupling TOPMODEL ≧ 1
TRIGRS ≧ 1
(1)
(3)
(2)
(4)
集水區西側(A區)之實際崩塌地分析成果
22
Discussion
集水區邊界
水系
艾利颱風崩塌地
Coupling TOPMODEL < 1
TRIGRS ≧ 1
Coupling TOPMODEL ≧ 1
TRIGRS < 1
Coupling TOPMODEL < 1
TRIGRS < 1
Coupling TOPMODEL ≧ 1
TRIGRS ≧ 1
(5)
(6)
(7)
集水區東側(B區)之實際崩塌地分析成果
23
Discussion
(8)
(9)
(11)
(10)
(12)
集水區邊界
水系
艾利颱風崩塌地
Coupling TOPMODEL < 1
TRIGRS ≧ 1
Coupling TOPMODEL ≧ 1
TRIGRS < 1
Coupling TOPMODEL < 1
TRIGRS < 1
Coupling TOPMODEL ≧ 1
TRIGRS ≧ 1
集水區北側(C區)之實際崩塌地分析成果
24
Conclusion
 本研究嘗試結合TOPMODEL將每個網格地下水位作適度
的修正,結果顯示結合TRIGRS與TOPMODEL能反應長
期暴雨期間側向補 注勢能對於整體地下水位分佈可能的
影響,分析結果顯示不穩定區多半遠離山脊而鄰近河岸,
與TOPMODEL之預期相符。
 研究中以馬莎颱風作為驗證分析模型的事件,馬莎颱風
誘發山崩雖較為不足,得到的預測率曲線下之AUC為
0.767,預測結尚屬合理。
 結合TRIGRS與TOPMODEL的總體正確率為89.4%,成
功率曲線下面積為0.822,TRIGRS的總體正確率為87.4%,
成功率曲線下面積AUC為0.787,從此兩種評估方法結果
可以看出,結合TRIGRS與TOPMODEL能有效地解釋崩
塌地分佈而可獲得較佳的預測效果。
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Thank you for your attention