development of a discharge prediction method based on topological case-based modeling and a...
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DEVELOPMENT OF A DISCHARGE PREDICTION METHOD BASED ON
TOPOLOGICAL CASE-BASED MODELING AND A DISTRIBUTED HYDROLOGICAL MODEL
Yamatake Corporation Kazuya HARAYAMA
Toshiaki OKA
DPRI, Kyoto University Toshiharu KOJIRI
Kenji TANAKA
Toshio HAMAGUCHI
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Contents
• Background
• Development of new method
• Application
• Conclusion
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Background
Methods for Predicting Discharge
TCBM( Topological Case-Based
Modeling)Application example :
Sewage inflow prediction Air conditioners control in buildings
(1) Runoff Model
+ Difficulty in determining
accurate parameters+ Long time required for model
construction
(2) Black Box Model
Rainfall
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Overview of TCBM
InputPrediction output
・・
・Modelx y
Able to predict the discharge
1.TCBM
2.Hydro-BEAM
3.Hydro-TCBM
Output・・
・
System( Unknown mechanism)
?x y
Discharge
Use only input-output relationship
Input
Example … Rainfall Hours w/o rainfall Temperature Day of the week …
Measurement data
RainfallD
isch
ar
ge
TCBM : Topological Case-Based Modeling
Modeling
Modeling data
Rainfall
Dis
cha
rg
e
Inputs with a strong connection to the output are selected by using stepwise method.
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Development objective
• In predicting discharge for an unprecedented heavy rainfall, the prediction error becomes large.
?
• New discharge prediction method was named Hydro-TCBM.
Creating a new case base by a simulation
Distributed hydrological model : Hydro-BEAM
Measurement data
Rainfall
Dis
cha
rg
e
How to solve TCBM’s main issue
1.TCBM
2.Hydro-BEAM
3.Hydro-TCBM
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Overview of Hydro-BEAM
waste water
surface runoff
precipitation
infiltration
evapo-transpiration
recovery flow
River flow
groundwater runoff
A Layer B Layer
C Layer
D Layer
Kinematic wave method
Linear storage method
Ground surface
1.TCBM
2.Hydro-BEAM
3.Hydro-TCBM
Hydro-BEAM : Hydrological River Basin Environmental Assessment Model
1 kilometer unit meshes
Flow direction of water
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Development of new method
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Development of a new discharge prediction method: Hydro-TCBM
Topological Case-Based Modeling
TCBM
Distributed Hydrological Model
Hydro-BEAM
Advantages: Data provided in real-timeOperation by PC
Advantages: Prediction for unprecedented rainfall
Case base 2Simulation results
Case base 1Historical data
Weather forecast
Prediction discharge
waste water
surface runoff
precipitation
infiltration
evapo-transpiration
recovery flow
River flow
groundwater runoff
Measurement data
Rainfall
Dis
cha
rg
e
1.TCBM
2.Hydro-BEAM
3.Hydro-TCBM
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Application
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Discharge estimation in Tama River basin3.Hydro-
TCBM
Flow measurement point
Tama River, Ishihara(Chofu-shi, Tokyo)
Period Apr 1, 2001 to Mar. 31, 2005
Interval 1 hour
Rainfall data Radar rainfall data
1.TCBM
2.Hydro-BEAM
Tama river basin
Notes
Area of the basin
1,240[km2]50th largest basin
area in Japan
Length of river 138[km]23rd longest river
in Japan
Annual average discharge
20.40[m3/sec]
Annual mean rainfall
1,532[mm]1,800[mm] :
Average in Japan
UrbanMountain
Land use
There are 109 large rivers in Japan.
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Selection of explaining variables by stepwise method3.Hydro-
TCBM
1.TCBM
2.Hydro-BEAM
Accumulated Time Series Rainfall for Each Mesh
1hour3hours6hours24hours32hours60hours92hours480hours720hours1440hours2160hours
1hour-1hour behind1hour-2hours behind1hour-3hours behind3hours-1hour behind3hours-2hours behind3hours-3hours behind6hours-1hour behind6hours-2hours behind6hours-3hours behind
163 meshes x 20 rainfall data= 3260 kinds of time series rainfall data
Five explaining variables are selected by the stepwise method
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Comparison of the difference in estimation accuracy by TCBM 3.Hydro-
TCBM
1.TCBM
2.Hydro-BEAM
3 years’ data 3 years’ datawithout heavy rainfall data
Expect low estimation accuracy in heavy rain
Expect high estimation accuracy
Case Basewith sufficient cases
Case Baselacking cases
Several heavy rainfall cases
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0
600
1,200
1,800
2,400
3,000
2004/ 9/ 16 2004/ 10/ 1 2004/ 10/ 16 2004/ 10/ 31 2004/ 11/ 15
Dis
char
ge [
m3 /s
ec]
0
20
40
60
80
100
Rai
nfal
l [m
m]
Rainfall Measured discharge Estimated discharge
Discharge estimation by TCBM1.TCBM
2.Hydro-BEAM
3.Hydro-TCBM
Discharge estimation using 3 years’ case base
Discharge estimation using 3 years’ case base
without heavy rainfall data
Root mean square error[%]
Maximum error [m3/sec]
TCBM with sufficient cases 1.4 618
TCBM w/o heavy rainfall data 3.1 1,049
Errors in discharge estimation
0
600
1,200
1,800
2,400
3,000
2004/ 9/ 16 2004/ 10/ 1 2004/ 10/ 16 2004/ 10/ 31 2004/ 11/ 15
Dis
char
ge [
m3 /s
ec]
0
20
40
60
80
100
Rai
nfal
l [m
m]
Rainfall Measured discharge Estimated discharge
low estimation accuracy
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Flow direction
Measurement point (Ishihara) Tokyo bay
1.TCBM
2.Hydro-BEAM
3.Hydro-TCBM
1km x 1km rectangle
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Calculation case and condition
case ExplanationMaximum rainfall per hour [mm]
3 years’ rainfall [mm]
case0 repetition 44 5,008
case1 1.5 times rainfall 66 7,512
case2 2.0 times rainfall 88 10,016
1.TCBM
2.Hydro-BEAM
3.Hydro-TCBM
Simulation
Accuracy validation
160
2,000
4,000
6,000
8,000
10,000
2001/ 8/ 1 2001/ 8/ 31 2001/ 9/ 30 2001/ 10/ 30
Dis
char
ge [
m3 /s
ec]
0
20
40
60
80
100
Rai
nfal
l [m
m/h
r]
Estimated discharge 2.0 times rainfall
0
2,000
4,000
6,000
8,000
10,000
2001/ 8/ 1 2001/ 8/ 31 2001/ 9/ 30 2001/ 10/ 30
Dis
char
ge [
m3 /s
ec]
0
20
40
60
80
100
Rai
nfal
l [m
m/h
r]
Estimated discharge 1.5 times rainfall
0
2,000
4,000
6,000
8,000
10,000
2001/ 8/ 1 2001/ 8/ 31 2001/ 9/ 30 2001/ 10/ 30
Dis
char
ge [
m3 /s
ec]
0
20
40
60
80
100
Rai
nfal
l [m
m/h
r]
Measured discharge Estimated discharge Rainfall
Discharge simulation by Hydro-BEAM
case0 (repetition)Accuracy Verification
case1(1.5 times rainfall)Simulation
case2(2.0 times rainfall)Simulation
Addition to case-base of Hydro-TCBM
1.TCBM
2.Hydro-BEAM
3.Hydro-TCBM
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0
600
1,200
1,800
2,400
3,000
2004/ 9/ 16 2004/ 10/ 1 2004/ 10/ 16 2004/ 10/ 31 2004/ 11/ 15
Dis
char
ge [
m3 /s
ec]
0
20
40
60
80
100
Rai
nfal
l [m
m]
Rainfall Measured discharge Estimated discharge by TCBM w/ o heavy rainfall
Discharge estimation by Hydro-TCBM1.TCBM
2.Hydro-BEAM
3.Hydro-TCBM
Root mean square error[%]
Maximum error [m3/sec]
TCBM w/o heavy rainfall data 3.1 1,049
Hydro-TCBM 2.1 762
Errors in discharge estimation
0
600
1,200
1,800
2,400
3,000
2004/ 9/ 16 2004/ 10/ 1 2004/ 10/ 16 2004/ 10/ 31 2004/ 11/ 15
Dis
char
ge [
m3 /s
ec]
0
20
40
60
80
100
Rai
nfal
l [m
m]
Rainfall Measured discharge Estimated discharge by Hydro-TCBM
Discharge estimation using Hydro-TCBM Discharge estimation using TCBM without heavy rainfall data
low estimation accuracy
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Conclusion
• TCBM has been used to improve accuracy in many fields. So it was applied to discharge estimation.
• Issue : Estimation accuracy decreased in an unprecedented heavy rainfall
• Solution : Adopt Hydro-BEAM to enhance the case base
• Development of Hydro-TCBM enabled us to raise the estimation accuracy close to that of TCBM without increasing measurement data.
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Thank you for your attention.
More information …
http://www.yamatake.com/profile/rd/tcbm/index.html
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4) Input space quantization is based on output error limit.
<Supplement> A discharge prediction based on TCBM (Modeling)
1) Output error limit
OUTPUT
INPUT
2) Output space quantization
3) Accumulate past cases
Completion of the prediction model
5) Average value calculated by using past cases each quantum
Discharge||
||Rainfall
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<Supplement> A discharge prediction based on TCBM (Prediction)
6) Scales up neighborhood, and searches for similar cases
OUTPUT
INPUT
Discharge||
||Rainfall