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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
1/6
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2/6
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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
4/7
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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
5/7
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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.jpg8/9/2019 SY Liong Lecture
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Types of Earthquake faults
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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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g
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.