HYDROASIA 2008 FLOOD ANALYSIS STUDY AT INCHEON GYO CATCHMENT TEAM GREEN NGUYEN HOANG HUYSUN YABIN GWON YONGHYEON SUZUKI ATSUNORI LI WENTAO LEE CHANJONG ADVISERS: Prof. LIONG SHIE YUI Prof. TANAKA KENJI

OUTLINE BACKGROUND OF CATCHMENT BACKGROUND OF CATCHMENT MODELING TOOLS MODELING TOOLS - SOBEK - MOUSE SIMULATION RESULTS SIMULATION RESULTS FORECASTING: NEURAL NETWORKS FORECASTING: NEURAL NETWORKS FORECAST RESULTS FORECAST RESULTS CONCLUSION CONCLUSION Q & A Q & A

INCHEON-GYO WATERSHED

−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 km 2 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 Hall Gansuk station Juan station Incheon Gyo Pump Station Coastline before 1984 Study Area Yellow Sea Incheon Gyo Pump station Reclamati on Area Incheon-gyo Catchment

MODELING 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

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 FORECAST SYSTEM

DefinitionDefinition An artificial neural network (ANN) is a mathematic model or computational model based on biological neural networks. 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. ANN consists of an interconnected group of nodes, akin to the vast network of neurons in the human brain.

ApplicationApplication  Function approximation  Regression analysis  Pattern recognition  Time series prediction

Schematic DiagramSchematic Diagram

ReferenceReference Haykin, S. (1999) Neural Networks: A Comprehensive Foundation, Prentice Hall, ISBN Haykin, S. (1999) Neural Networks: A Comprehensive Foundation, Prentice Hall, ISBN

THE RESULT OF NEURAL NETWORK

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 INPUTOUTPUT RainfallTotal Discharge TT-dtT-2dtTT-dtT-2dtT+dt, T+2dt Dt=30 minutes Scenarios RainfallWL at pond Training 100% 50%100% 120%100% 120%50% 100%50% Validation100%120% Neural Network setup for input and output

Maximum rainfall intensity 50%57 100% %136.8

DISCHARGE S AT RECERVOIR OF THREE MAIN METWORKS (4 August 1997)

TrainingValidation LeadtimeCCR2CCR2 30 mins mins Correlation coefficient 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

Conclusion MOUSE and SOBEK have been used to study Incheon catchment for the event in 1997.MOUSE and SOBEK have been used to study Incheon catchment for the event in Several scenarios have been successfully generated by both MOUSE and SOBEK.Several scenarios have been successfully generated by both MOUSE and SOBEK. Present an idea of using neural network at a forecast system for reservoir operationPresent 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.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.Discharge at the next time step has been reasonably predicted by ANN. Suggest some solutions to improve the forecast systemSuggest some solutions to improve the forecast system

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