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

4

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