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HYDROASIA 2008 FLOOD ANALYSIS STUDY AT INCHEON GYO CATCHMENT. TEAM GREEN NGUYEN HOANG HUY SUN YABIN GWON YONGHYEON SUZUKI ATSUNORI LI WENTAO LEE CHANJONG. ADVISERS: Prof. LIONG SHIE YUI Prof. TANAKA KENJI. OUTLINE BACKGROUND OF CATCHMENT MODELING TOOLS - SOBEK

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HYDROASIA 2008

FLOOD ANALYSIS STUDY AT

INCHEON GYO CATCHMENT

TEAM GREEN

NGUYEN HOANG HUY SUN YABIN

GWON YONGHYEON SUZUKI ATSUNORI

LI WENTAO LEE CHANJONG

ADVISERS: Prof. LIONG SHIE YUI

Prof. TANAKA KENJI


  • OUTLINE

  • BACKGROUND OF CATCHMENT

  • MODELING TOOLS

  • - SOBEK

  • - MOUSE

  • SIMULATION RESULTS

  • FORECASTING: NEURAL NETWORKS

  • FORECAST RESULTS

  • CONCLUSION

  • Q & A



Incheon

  • 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


Gaja WWTP

Pump Station

Incheon Gyo

Coastline before 1984

Yellow Sea

Juan station

Gansuk station

Study area

City Hall

Reclamation Area

Pump station

Study Area

Incheon Gyo

Incheon-gyo Catchment

  • 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)



MOUSE SETUP

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

  • Setting up Urban Drainage model with MOUSE

  • Validation


Input Rainfall Data

  • 4/8/1997 1AM ~ 4/8/1997 4PM (15 hrs)

  • Maximum rainfall : 19mm/10min

100%



WATER ON STREET AT NODES (MANHOLES)

MANHOLES AT FLOOD AREA





WATER ON STREET AT NODES (MANHOLES)

NODES NOT AT FLOOD AREA


WATER ON STREET AT NODES (MANHOLES)

NODES NOT AT FLOOD AREA









  • Definition

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

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


  • Application

    • Function approximation

    • Regression analysis

    • Pattern recognition

    • Time series prediction



  • Reference

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




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

Neural Network setup for input and output

Dt=30 minutes


DISCHARGE S AT RECERVOIR OF THREE MAIN METWORKS forecast the total discharge at the reservoir. The data series are splitted into 2 portions, one for training while the other for validation

(4 August 1997)


Correlation coefficient forecast the total discharge at the reservoir. The data series are splitted into 2 portions, one for training while the other for validation

R squared


SOBEK SIMULATED VS ANN FORECAST forecast the total discharge at the reservoir. The data series are splitted into 2 portions, one for training while the other for validation

30 minutes leadtime


SOBEK SIMULATED VS ANN FORECAST forecast the total discharge at the reservoir. The data series are splitted into 2 portions, one for training while the other for validation

60 minutes leadtime


Rainfall forecast the total discharge at the reservoir. The data series are splitted into 2 portions, one for training while the other for validation & Wind Forecasting

Catchment Runoff & Sea Level Forecasting

Optimal Reservoir Operation

SUGGESTIONS

Online forecast system


Conclusion
Conclusion forecast the total discharge at the reservoir. The data series are splitted into 2 portions, one for training while the other for validation

  • MOUSE and SOBEK have been used to study Incheon catchment for the event in 1997.

  • Several scenarios have been successfully generated by both MOUSE and SOBEK.

  • Present an idea of using neural network at a forecast system for reservoir operation

  • An Artificial Neural Network model has been trained by the scenarios generated with sense.

  • Discharge at the next time step has been reasonably predicted by ANN.

  • Suggest some solutions to improve the forecast system


THANK YOU forecast the total discharge at the reservoir. The data series are splitted into 2 portions, one for training while the other for validation

Q & A


Our team movie forecast the total discharge at the reservoir. The data series are splitted into 2 portions, one for training while the other for validation


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