HYDROASIA 2008

FLOOD ANALYSIS STUDY AT

INCHEON GYO CATCHMENTTEAM GREEN

NGUYEN HOANG HUY SUN YABIN

GWON YONGHYEON SUZUKI ATSUNORI

LI WENTAO LEE CHANJONGADVISERS: Prof. LIONG SHIE

YUIProf. TANAKA KENJI

OUTLINEOUTLINE• BACKGROUND OF CATCHMENTBACKGROUND OF CATCHMENT• MODELING TOOLS MODELING TOOLS

- SOBEK- SOBEK

- MOUSE- MOUSE• SIMULATION RESULTSSIMULATION RESULTS• FORECASTING: NEURAL NETWORKSFORECASTING: NEURAL NETWORKS• FORECAST RESULTSFORECAST RESULTS• CONCLUSIONCONCLUSION• Q & AQ & A

INCHEON-GYO INCHEON-GYO WATERSHEDWATERSHED

− Located in the mid-west Korea peninsula near Yellow Sea

− With both international port and international airport

− The third biggest city in Korea

− Population : 2,730 thousand

Incheon

– Total area : 34 km2 Length :8 km– Tidal difference : 9 m– Avg. of Rainfall : 1,702.3 mm/year– Most of present Incheon Gyo watershed was sea before

completed to reclamation in 1985– Reclamation area used for industry & residence– Culvert slope is very mild(Avg. of Slope : 0.01 %)– Flooding in 1997 to 2001 (except 2000)

Study area

Gaja WWTP

City HallGansuk station

Juan station

Incheon Gyo

Pump Station

Coastline before 1984

Study Area

Yellow Sea

Incheon Gyo

Pump station

Reclamation Area

Incheon-gyo Catchment

MODELING TOOLSMODELING TOOLS

MOUSE SETUP

• Import from the excel file “Imported data to Mouse.xls” to Mouse

• Setting up Urban Drainage model with MOUSE

• Validation

• 4/8/1997 1AM ~ 4/8/1997 4PM (15 hrs)• Maximum rainfall : 19mm/10min

Input Rainfall Data

100%

Flood(100_100)

WATER ON STREET AT NODES (MANHOLES)MANHOLES AT FLOOD AREA

SIDE VIEW OF SIMULATION RESULTS

SIDE VIEW OF SIMULATION RESULTS

SOBEK SET UP

WATER ON STREET AT NODES (MANHOLES)NODES NOT AT FLOOD AREA

WATER ON STREET AT NODES (MANHOLES)NODES NOT AT FLOOD

AREA

SIDE VIEW OF SIMULATION RESULTS

WATER ON STREET AT NODES (MANHOLES)NODES AT FLOOD

AREA

WATER ON STREET AT NODES (MANHOLES)NODES AT FLOOD AREA

SIDE VIEW OF SIMULATION RESULTS

WATER ON STREET AT NODES (MANHOLES)NODES AT FLOOD

AREA

SIDE VIEW OF SIMULATION RESULTS

USING NEURAL NETWORK AS A USING NEURAL NETWORK AS A FORECAST SYSTEMFORECAST SYSTEM

• DefinitionDefinition

An artificial neural network (ANN) An artificial neural network (ANN) is a is a mathematic model mathematic model or or computational model computational model based on based on biological neural networks.biological neural networks.

ANN consists of an interconnected ANN consists of an interconnected group of nodes, akin to the vast group of nodes, akin to the vast network of network of neuronsneurons in the human in the human brain.brain.

• ApplicationApplication

Function approximation Function approximation Regression analysisRegression analysis Pattern recognitionPattern recognition Time series predictionTime series prediction

• Schematic DiagramSchematic Diagram

• ReferenceReference

Haykin, S. (1999) Haykin, S. (1999) Neural Networks: Neural Networks: A Comprehensive FoundationA Comprehensive Foundation, , Prentice Hall, ISBN 0-13-273350-1Prentice Hall, ISBN 0-13-273350-1

THE RESULT OF NEURAL THE RESULT OF NEURAL NETWORKNETWORK

WHY A FORECAST SYSTEM IS NEEDED?

The Multilayer Perceptron Neural Network is then used to forecast the total discharge at the reservoir. The data series are splitted into 2 portions, one for training while the other for validation

INPUT OUTPUT

Rainfall Total Discharge Total Discharge

T T-dt T-2dt T T-dt T-2dt T+dt, T+2dt

Dt=30 minutes

 Scenarios

Rainfall WL at pond

Training

100% 100%50% 100%120% 100%120% 50%100% 50%

Validation 100% 120%

Neural Network setup for input and output

Maximum rainfall intensity50% 57100% 114120% 136.8

DISCHARGE S AT RECERVOIR OF THREE MAIN METWORKS

(4 August 1997)

  Training ValidationLeadtime CC R2 CC R2

30 mins 0.97 0.93 0.8 0.63

60 mins 0.92 0.83 0.54 0.2

1

2 2

1 1

N

i ii

N N

i ii i

O O F FCC

O O F F

Correlation coefficient

2 1

1

1

N

i iiN

i ii

O FR

O F

R squared

SOBEK SIMULATED VS ANN FORECAST

30 minutes leadtime

60 minutes leadtime

SOBEK SIMULATED VS ANN FORECAST

SUGGESTIONS

Rainfall & Wind Forecasting

Catchment Runoff & Sea Level Forecasting

Optimal Reservoir Operation

Online forecast system

ConclusionConclusion• MOUSE and SOBEK have been used to study MOUSE and SOBEK have been used to study

Incheon catchment for the event in 1997.Incheon catchment for the event in 1997.• Several scenarios have been successfully generated Several scenarios have been successfully generated

by both MOUSE and SOBEK.by both MOUSE and SOBEK.• Present an idea of using neural network at a forecast Present an idea of using neural network at a forecast

system for reservoir operationsystem for reservoir operation• An Artificial Neural Network model has been trained An Artificial Neural Network model has been trained

by the scenarios generated with sense.by the scenarios generated with sense.• Discharge at the next time step has been reasonably Discharge at the next time step has been reasonably

predicted by ANN.predicted by ANN.• Suggest some solutions to improve the forecast Suggest some solutions to improve the forecast

systemsystem

THANK YOUTHANK YOUQ & AQ & A

Our team movieOur team movie