SY Liong Lecture

8/9/2019 SY Liong Lecture

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Content

Introduction* Water resources problem

* Tsunami forecasting problem

Coping with the above engineering

problem

Mining hydrologic and hydraulic data

Conclusions


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S

Singapore

Introduction


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Strait of Johor


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Geology of SEA (Sumatra Subduction Zone)

Tsunami


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Indian ocean tsunami


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A. Water Resources Problem in Singapore

Size: 700 km2

Population: 4.5 Mill ions

30% of (river) water imported from Malaysia

Coping water resources problem through: Water catchment

Desalination plant

Recycled water Damming bays


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S

Singapore


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Strait of Johor


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Marina Bay


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Marina Bay

Barrage


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Marina Bay

Barrage


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Current (Marina)


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Astronomical Tide

Hourly tidal data for one month: 1st 31st January 2006


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Tide Information - 22 Dec 99

0

0.5

11.5

2

2.5

3

3.5

4

0

2

0

0

4

0

0

6

0

0

8

0

0

10

0

0

12

0

0

14

0

0

16

0

0

18

0

0

20

0

0

22

0

0

Time

Tid

e

(m

) Predicted

Observed

Flooding occurred due to storm surge!


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Marina Catchment


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Marina

BarragePump

Station


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Marina

BarragePump

Station


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Marina

BarragePump

Station


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Marina

BarragePump

Station


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With Barage my ha

Barrage Operation must consider impact of

storm surge


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Significant wave height and mean wave direction

Hs (cm)

x(km)

y(km)

44 46 48 50 52 54 56 58 60 62

22

24

26

28

30

32

34

5

10

15

20

25

30

35

40

45

10%

20%

30%

40%

30

210

60

240

90

270

120

300

150

330

180 0

(m/s)Calm (42%)0 1.0 3.0 5.0 7.0 9.0 11.0 13.0

Wind speed: 7.2 m/s; and wind direction: 300


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PROJECT COMPONENTS

Rainfall & Wind Forecasting

Catchment Runoff & Sea Level Forecasting

INTEGRATION of all Components requires GIS and other DSS tools

Optimal Reservoir Operation


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Data Collection Processing Model

Simulation Mimicking simulated datathrough data driven technique (absolutely

necessary for operational system)

Geographic Information System

RAINFALL & WIND

RUNOFF

SEA LEVEL


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Integration

Hs (cm)

x(km)

y(km)

44 46 48 50 52 54 56 58 60 62

22

24

26

28

30

32

34

0

5

10

15

20

25

30

35

40

Prediction

&

Simulation

INTEGRATION


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Data & GIS

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Organization of

Thematic layers to GIS

Climate data to database Interface database with GIS

Custom tool interface for rainfall data

visualization


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MARINA DRAINAGE AND CATCHMENTS

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DIGITAL ELEVATION MODEL

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Rainfall data

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Rainfall and Wind

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Runoff Data

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Marina catchment with sub catchments

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Flow Monitoring Sites


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Flow Monitoring Sites

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S f C ti P fil


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Survey of Cross-section Profile

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Survey of Cross-section Profile

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Flow Gauging Instrument

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M ti T k f Fl G i I t t


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Mounting Track of Flow Gauging Instrument

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Wind Generated Wave Data and

Simulation

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Wind Rose Study (resolution:30)


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Wind Rose Study (resolution:30 )

10%

20%

30%

40%

30

210

60

240

90

270

120

300

150

330

180 0

(m/s)Calm (42%)0 1.0 3.0 5.0 7.0 9.0 11.0 13.0

Wind rose based on hourly wind data:

Dominant wind direction (270-300

); Maximum wind speed: 7.2 m/s

Mean wind speed: 2.6 m/s 2/7

Significant wave height and mean wave direction


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Hs (cm)

x(km)

44 46 48 50 52 54 56 58 60 62

22

24

26

28

30

32

34

5

10

15

20

25

30

35

40

45

10%

20%

30%

40%

30

210

60

240

90

270

120

300

150

330

180 0

(m/s)Calm (42%)0 1.0 3.0 5.0 7.0 9.0 11.0 13.0

Wind speed 7.2 m/s; wind direction 3003/7

Study Domain and Sample Points for


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y p

Data-Driven Model Construction

Bathymetry of Singapore Strait;

Domain of interest is marked by

red box:Domain of interest with 15

sample points for data-driven model construction

and validation:

x(km)

y(km)

Depth (m)

Nest

1+

2+

3+

4+

5+

6+

7+

8+

9+

10+

11+12+

13+14+

15+

44 46 48 50 52 54 56 58 60 62

22

24

26

28

30

32

34

0

20

40

60

80

100

120

140

160

180

200

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x(km)

y(km)

Depth (m)

Nest

1+

2+

3+

4+

5+

6+

7+

8+

9+

10+

11+12+

13+14+

15+

44 46 48 50 52 54 56 58 60 62

22

24

26

28

30

32

34

0

20

40

60

80

100

120

140

160

180

200

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Validation result at Point 2


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20 40 60 80 1000

1

2

3

4

5

6

Validation events

Meanwa

veperiod(sec)

SWAN

ANN

0 2 4 60

1

2

3

4

5

6

SWAN (sec)

A

NN(sec)

20 40 60 80 1000

5

10

15

20

Validation events

Wavelength(m)

SWAN

ANN

0 5 10 15 200

5

10

15

20

SWAN (m)

ANN(m)

R2=0.9825

Comparison of data-driven model result (ANN) with

SWAN model result

Scatter diagram of data-driven model result

(ANN) and SWAN model result

R2=0.9828

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B. Tsunami Forecasting Problem

Past Tsunami Events


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Essential Steps

A. Determine potential source of earthquake that will cause tsunami

B. Define critical fault lines potentially affecting area of interest

C. Obtain key parameters along this fault line for tsunami generation

D. Differentiate tsunamigenic earthquakes from non-tsunamigenicearthquakes

E. Simulate tsunami numerical model(s)

F. Apply data driven techniques to mimic tsunami numerical models

(essential for operational system)

Fault-lines


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Geology of South-East Asia

Seismic Stations


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http://nctr.pmel.noaa.gov/Dart/Jpg/dart_mooring0.jpg


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Types of Earthquake faults
http://nctr.pmel.noaa.gov/Dart/Jpg/dart_mooring0.jpg


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Normal Fault Reverse Fault

STRIKE-SLIP FAULT OBLIQUE-SLIP FAULT

DIP-SLIP

FAULT

Types of Earthquake faults = +90o = -90

o

= 0o-180


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p

Three basic stages of


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tsunami processes

PROPAGATION

GENERATION

INUNDATION

BREAKING

Animation Indian ocean tsunami


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Animation Indian ocean tsunami


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Soft computing technique

in

NORTH SUMATRA TSUNAMI26 DEC 2004


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Study

areas

Longitude

between

-4.5 and

23,7degree

Wave arrival time (hours) Fault1


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Wave arrival time (hours) Fault1

Maximum wave height (m) Fault 1

W i l i (h ) F l 1 2


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Wave arrival time (hours) Faults 1+2

Maximum wave height (m) Faults 1+2

Summary


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Summary

Two engineering problems were discussed:Water Resources and Tsunami operationalsystems

Data collection, processing, analysis, numericalmodel calibration & simulations for scenarios of

interest are some of main essential steps insolving engineering problems.

For many operational systems, having an

effective and efficient computational tool isessential.

SY Liong Lecture

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