the utilization of gis for the measure against slope failure disaster in urban area akiyuki...
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The Utilization of GIS for the The Utilization of GIS for the Measure against Slope Failure Measure against Slope Failure
Disaster in Urban Area Disaster in Urban Area
Akiyuki KAWASAKI, and Satoru SADOHARAAkiyuki KAWASAKI, and Satoru SADOHARAYokohama National University, Yokohama, JapanYokohama National University, Yokohama, Japan
BackgroundBackground
⇒ It’s difficult for a municipality to afford it.
・ Many people and much budget are required to accomplish this kind of project (so far).
⇒ Municipality needs a measure against slope failure disaster
・ Many slope failure disasters happen in Yokohama
(the ultimate goal)
・ To createcreate a “Real-time evaluation system against slope failure disasters” by by existing data and documentsexisting data and documents in municipal government..
⇒ It will reduce the damage of slope failure disasters wiwith a small budget and people.th a small budget and people.
ObjectiveObjective
AA.. SpecifyingSpecifying the “steep slope” and “the predicted damage a the “steep slope” and “the predicted damage area” automatically rea” automatically by DEM and LandUse data using GIS using GIS raster raster & vector analysis& vector analysis..
B. B. Predicting the dangerous area for slope failure the dangerous area for slope failure by by rainfall data at the timerainfall data at the time using GIS spatial analysis. using GIS spatial analysis.
For that objective, three For that objective, three methodologies were carrying forward methodologies were carrying forward in this study.in this study.
(Presented last year)
(Presented last year)
C. Evaluate the possibility of slope failure disaster, using Inspection Record and Multivariate Analysis.
((Presented this yearthis year))
被被被災災災危危危険険険区区区域域域ののの抽抽抽出出出 崖崖崖区区区域域域ののの抽抽抽出出出
i.最大値
j.最小値
h. GRID
d.TIN e.TIN ポリゴン
p.被災 危険区域
k.高さを算出
f.急傾斜地 ポリゴン
l.高さ GRID
m.崖ポリゴン
(属性:区域・高さ)
(属性:斜面方向・高さ)
a.土地利用
元元元デデデーーータタタ c.自然的土地 利用ポリゴン
b.DEM
g.崖区域 ポリゴン
e.斜面方向 ポリゴン ‘
m. 崖ポイント ‘ o.斜面方向の ライン作成
n.バッファ の発生
Steep Slope Predicted-damaged area
Software: ArcInfo Workstation & ArcView
LandUseLandUse
DEMDEM
Steep Steep slopeslope
PredictedPredicted
damaged damaged areaarea
AA..Methodology of Methodology of specifyingspecifying the “steep the “steep slope” and “the predicted damage area” slope” and “the predicted damage area” automatically.automatically.
(Review)
This is helpful to predict which building and house would be damaged by collapse. Only two data is required.
AA..Methodology of Methodology of specifyingspecifying the “steep the “steep slope” and “the predicted damage area” slope” and “the predicted damage area” automaticallyautomatically..
(Review)
These are the specified dangerous steep slopes⇒We verified that it covers more than 90% of (existing)
“ Municipality’s dangerous areas”
AA..Methodology of Methodology of specifyingspecifying the “steep the “steep slope” and “the predicted damage area” slope” and “the predicted damage area” automaticallyautomatically..
(Review)
Predicted damage area is calculated by the height of the slope. It means that higher steep slope has longer damaged area.
AA..Methodology of Methodology of specifyingspecifying the “steep the “steep slope” and “the predicted damage area” slope” and “the predicted damage area” automaticallyautomatically..
(Review)
Predicted damage area is calculated by the height of the slope. It means that higher steep slope has longer damaged area.
B. Methodology of pB. Methodology of predicting the dangerous the dangerous area for slope failure by area for slope failure by rainfall data at the rainfall data at the timetime. .
(Review)
Dangerous Area at the time by Rainfall data ( August/22th/2001 )
2.00 am 6.00 am 10.00 am 2.00 pm
1 hour rainfall (mm)
Y = ‐0.174X + 28.4
(“Raster Calculation” )
AA.. Dangerous steep slope and predicted Dangerous steep slope and predicted damage area are specified.damage area are specified.
B. Relationship between collapse and rainfall are analyzed.
C. C. Relationship between collapse and the Relationship between collapse and the slope’s factorsslope’s factors..
⇒⇒ Provoking causeProvoking cause 《《 Triggered factorTriggered factor》》 for collapsefor collapse
⇒⇒ Primary causePrimary cause 《《 Basic factorBasic factor》》 for collapsefor collapse
For the objective, three methodologies For the objective, three methodologies were carrying forward in this study.were carrying forward in this study.
⇒ Automatic specification.
Evaluate the primary cause (basic factor), using Inspection Report and Multivariate Analysis; Quantification Theory TypeⅡ.
Easy to collapse
Difficult to collapse
C. C. Relationship between collapse Relationship between collapse and the slope’s factors and the slope’s factors . .
(Provided by City of Yokohama, Bureau of Building)
Inspection Report for a Steep Slope
- Height
- Slope
- Overhang
- Surface thickness
-Leaking water
- Vegetation
- Looseness, relaxation
- Surface water
- Drainage on the top
10 items are quantified quantified
by report’s category.
Collapse Collapse
recordrecord
Multivariate Analysis; Quantification Theory TypeⅡ
This model clarify qualityquality “explanatory variable “explanatory variable (item)”(item)” affecting qualityquality “objective variable”“objective variable” ,as a quantityquantity weighted coefficient.
““explanatory variable (item)”explanatory variable (item)” : 10 items on inspection report
““objective variable”objective variable” : Collapse record (collapsed or not)
Example of analysis result
0.0
0.5
1.0
1.5
Heig
ht
Slo
pe
Su
rface
Leakin
gw
ate
r
Loose
ness
rela
xati
on
Vag
itati
on
Su
rface W
ate
r(a
bove)
Su
rface W
ate
r(s
lop
e)
Dra
inag
e o
n t
op
Over
Han
g
Pattern 1 Pattern 2 Pattern 4Range
Factor’s effectiveness for collapse
パターン1
Surface
Roam
Pattern 1
slump
パターン1
Surface
Roam
Pattern 1パターン1
Surface
Roam
Pattern 1
slump
パターン2
Surface
Roam
Pattern 2
Plat form Plat form layerlayer
slide
パターン2
Surface
Roam
Pattern 2パターン2パターン2
Surface
Roam
Pattern 2
Plat form Plat form layerlayer
slide
パターン4
Surface
Pattern 4
Plat form Plat form layerlayer
slide
パターン4
Surface
Pattern 4
Plat form Plat form layerlayer
パターン4
Surface
Pattern 4パターン4
Surface
Pattern 4
Plat form Plat form layerlayer
slide
Slope Failure Pattern in Yokohama
(Categorized by “Yokohama city slope failure committee”)(Categorized by “Yokohama city slope failure committee”)
Item CategoryNum. ofsample
Weightdcoefficient
Rangepartial
correlationcoefficient
10m - 24 - 0.3785m - 10m 38 0.086
- 5m 12 0.48560° - 23 0.272
40° - 60° 44 - 0.107- 40° 7 - 0.21950cm - 8 0.644- 50cm 66 - 0.078Exist 4 1.296No 70 - 0.074
Exist 8 1.207No 66 - 0.146
Bare ground 17 - 0.346tree 5 0.151
grass/ field 11 0.278tree grass/ field+ 41 0.050
Exist 35 0.190No 39 - 0.171
Exist 42 0.160No 32 - 0.209
Bad/ Uncompleted 54 0.013Good 20 - 0.036Exist 7 0.018No 67 - 0.002
0.224
0.125
0.005
0.171
0.255
0.311
0.159
0.134
0.863
0.491
0.020
0.049
0.369
0.361
0.624
0.722
1.353
1.370
Height of Slope(Hm)
Slope(degree)
Over Hang
Thicknessof Surface
Leaking Water
LoosenessRelaxation
Vegitation
Surface Water(above)
Surface Water(slope)
Drainage on top
0.158
0.018
Results of Quantification Theory TypeⅡ
0.0
0.1
0.2
0.3
0.4
崩壊あり 崩壊なし
-2.0 - 1.5 - 1.0 - 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
Sample score
Rela
tive f
requency
Frequency distribution of distinction result
Actual
collapsed
No collapse
Discriminate Value – 0.06
(Accuracy 85.1%)
Pattern 1
Surface
Roam
Surface
Roam
Sample score : Total of weighted coefficient of a slope
0.086+(-0.107)+(-0.078)+1.296+……=0.677
パターン1
Surface
Roam
Pattern 1
slump
パターン1
Surface
Roam
Pattern 1パターン1
Surface
Roam
Pattern 1
slump
パターン2
Surface
Roam
Pattern 2
Plat form Plat form layerlayer
slide
パターン2
Surface
Roam
Pattern 2パターン2パターン2
Surface
Roam
Pattern 2
Plat form Plat form layerlayer
slide
パターン4
Surface
Pattern 4
Plat form Plat form layerlayer
slide
パターン4
Surface
Pattern 4
Plat form Plat form layerlayer
パターン4
Surface
Pattern 4パターン4
Surface
Pattern 4
Plat form Plat form layerlayer
slide
Slope Failure Pattern in Yokohama
(Categorized by “Yokohama city slope failure committee”)(Categorized by “Yokohama city slope failure committee”)
Discriminate Value – 0.06 (Accuracy 85.1%)
Discriminate Value – 0.01
(Accuracy 84.2%)
Discriminate Value 0.01
(Accuracy 82.1%)
Sample score
Rel
ativ
e fr
eque
ncy
Actual collapsed
No collapse
0.0
0.1
0.2
0.3
0.4 崩 壊 有 り 崩 壊 な し
- 2.0 - 1.5 - 1.0 - 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
Sample score
Rel
ativ
e fr
eque
ncy
Actual collapsed
No collapse
0.0
0.1
0.2
0.3
0.4 崩 壊 有 り 崩 壊 な し
- 2.0 - 1.5 - 1.0 - 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
Frequency distribution of distinction result
0
0.1
0.2
0.3
0.4
0.5 崩 壊 有 り 崩 壊 なし
- 2.0 - 1.5 - 1.0 - 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
Sample score
Rel
ativ
e fr
eque
ncy
Actual collapsed
0
0.1
0.2
0.3
0.4
0.5 崩 壊 有 り 崩 壊 なし
- 2.0 - 1.5 - 1.0 - 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
Sample score
Rel
ativ
e fr
eque
ncy
Actual collapsed
Sample score
Rel
ativ
e fr
eque
ncy
Actual collapsed
No collapse
0.0
0.1
0.2
0.3
0.4 崩 壊 有 り 崩 壊 な し
- 2.0 - 1.5 - 1.0 - 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
Sample score
Rel
ativ
e fr
eque
ncy
Actual collapsed
No collapse
0.0
0.1
0.2
0.3
0.4 崩 壊 有 り 崩 壊 な し
- 2.0 - 1.5 - 1.0 - 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
- Factors and the category’s effectiveness for collapse were clarified quantitatively.
- Accuracy of distinction for collapse by sample score was over 80%
⇒⇒ Municipality’s Inspection report woks adequMunicipality’s Inspection report woks adequately to evaluate the possibility of collapse.ately to evaluate the possibility of collapse.
C. C. Relationship between collapse Relationship between collapse and the slope’s factors and the slope’s factors . .
Conclusion 1.Conclusion 1.
⇒ ⇒ By combining these three, a “Real-time evaluation systeBy combining these three, a “Real-time evaluation system against slope failure disasters” would be completed m against slope failure disasters” would be completed
AA.. Dangerous steep slope and predicted Dangerous steep slope and predicted damage area are specified.damage area are specified.
B. Relationship between collapse and rainfall are analyzed.
C. Relationship between collapse and the slope’s factors.
⇒⇒ Provoking causeProvoking cause 《《 Triggered factorTriggered factor》》 for collapsefor collapse
⇒⇒ Primary causePrimary cause 《《 Basic factorBasic factor》》 for collapsefor collapse
⇒ Automatic specification.
Specify dangeroushouse
Rain Fall
InspectionResults
Records ofSlope Failure
Specify thedangerous area
1. Specifying the Dangerous Area by Rain Fall Data
Possibility ofSlope Failure
InspectionResults
2. Possibility of Slope Failure
InspectionResults
Records ofSlope Failure
PredictedDamage Area
3. Specifying the Predicted Damage Area
Future worksFuture works
Conclusion 2.Conclusion 2.
All the methodology in this study was from the All the methodology in this study was from the existing data and document in municipal existing data and document in municipal government.government.⇒⇒ Original data and document are existed separately in eaOriginal data and document are existed separately in each bureau and section. In this study, all the data was digitized ch bureau and section. In this study, all the data was digitized and connected.and connected.
““e-government” and “e-municipal government” are declared ie-government” and “e-municipal government” are declared in Japan.n Japan.
⇒⇒ This is an example to show the efficiency of digitizing aThis is an example to show the efficiency of digitizing and utilizing the existing data and document in municipal govend utilizing the existing data and document in municipal government. rnment.
Akiyuki KAWASAKI, and Satoru SADOHARAAkiyuki KAWASAKI, and Satoru SADOHARAYokohama National University, Yokohama National University,
Yokohama, JapanYokohama, JapanE-mail: [email protected]: [email protected]
[email protected]@arc.ynu.ac.jp
AcknowledgementAcknowledgement
- Risk management office & Building Bureau, City of Yokohama
- Prof. Midorikawa & Prof. Okimura, Yokohama city slope failure committee