Transcript Title Of Presentation (Verdana font size 28, bold)

Sensor Placement and
Measurement of Wind for Water
Quality Studies in Urban Reservoirs
Wan DU*, Zikun XING†, Mo LI*, Bingsheng HE*,
Loyd Hock Chye CHUA†, and Haiyan MIAO‡
* School of Computer Engineering, Nanyang Technological University (NTU)
† School of Civil and Environmental Engineering, NTU
‡ Institute of High Performance Computing, A*Star, Singapore
Large-scale and real-time water quality
monitoring
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Patterns
of interest
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Results
Cloud computing
• Sustainable sensor network deployment.
• Water quality analysis enabled by cloud
computing.
2
Marina reservoir
Marina Reservoir
Kallang Basin
10%
10%
Marina Bay
2.5km
3
3km
Water quality studies
Environmental
parameters
including wind
distribution and
water quality
Water quality
in the whole
reservoir
Ecological model
Underwater sensors, e.g., DO, Conductivity, Chlorophyll, pH, temperature
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Water quality studies - deployment
Solar charger controller
Project demo video
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Data collection
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Data collection
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Water quality studies
• ELCOM-CAEDYM (Estuary, Lake and Coastal
Ocean Model-Computational Aquatic
Ecosystem Dynamics Model)
• Real time monitoring
• Analysis
• Prediction
– Water quality evolution for future days in a step of 30
seconds
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Effect of wind on water quality
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Wind distribution over Marina reservoir
Marina Reservoir
Marina Bay
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Kallang Basin
Measurement of wind distribution
18750 points (20m*20m));
6000$/ground station; 7600$/floating station;
Long measurement time (at least one year)
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Sensor placement and spatial prediction
Where?
How?
Wind distribution with least
uncertainty
Water quality studies
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Spatial prediction
Wind?
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Interpolation
d2
d1
Wind?
d3
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Spatial correlation [Cressie, Statistics for
spatial data’ 91; Guestrin, ICML’ 05; Krause,
IPSN’ 06, 08]
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Maximum likelihood based time series
segmentation
NE
06-07: Dec.2
07-08: Dec.14
Pre-SW
Mar.15
Jun.1
Mar.28
Jun.3
SW
Pre-NE
Dec.13
Oct.1
Dec.6
Sep.27
L( 1 ,  1 ,  2 , 3 ,  3 ,  4 | x1 , x2 ,, xM  N  K  J )
M

i 1
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
M N K

iM  N
 1
1
2  1 


exp 
x


i
1 

2
2

2 1
1

  2 
N
 1
1
2  1 


exp 
x


i
3 

2
2

2 3
1

  4 
J
Maximum likelihood based time series
segmentation
NE
06-07: Dec.2
07-08: Dec.14
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Pre-SW
Mar.15
Jun.1
Mar.28
Jun.3
SW
Pre-NE
Dec.13
Oct.1
Dec.6
Sep.27
Spatial correlation [Cressie, Statistics for
spatial data’ 91; Guestrin, ICML’ 05; Krause,
IPSN’ 06, 08]
ij  M ( xi , x j )
 ij  ij  k ( xi , x j )
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Prior knowledge of wind distribution
Atmospheric flow
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Pairwise correlation learning
• 16 point compass rose
• 10 speeds (0-9m/s)
• Historical data of the sensor
on Marina Channel
ij  M ( xi , x j )
 ij  ij  k ( xi , x j )
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Sensor placement
H ( xi )    p( x) log p( x)
xX
1
H ( yi | A)  log( 2e y2i |A )
2
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Combining the results of multiple
Gaussian processes
• Entropy at one point:
3
H ( xi )   H ( xij ) *W j
j 1
• Conditional entropy:
3
H ( yi | A)   H ( yij | A) *W j
j 1
23
Sensor placement - Water quality
sensitivity
H ( xi )  H ( xi ) * Si
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H ( xi | A)  H ( xi | A) * Si
Approach overview
(7)
Wind distribution
of the whole area
(3)
CFD
modeling
Gaussian
Regression
(1)
Historical wind
direction density
Geographical
information
system
(2)
Time Series
Segmentation
Decomposed wind
statistics
Entropy or
Mutual
Information
(4)
Sensor
Placement
Sensitivity Analysis
Online temporal
clustering
(6)
Real-time Sensor
Readings
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(5)
Data Collection
Enhanced Sensor
Placement
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Predicted wind distribution
Direction
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Speed
Evaluation
• Prediction accuracy
– Interpolation
– Single Gaussian model
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10
11
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12
9
8
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13
15
7
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14
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20
6
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1
17
5
2
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4
Installed Sensor (Floating or Ground)
Test Position
3
Average prediction error of direction
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Average prediction error of speed
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Prediction error VS
Water quality sensitivity
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Water temperature
Improve the accuracy by 17% in Marina Basin
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Conclusions
• Sensor placement for wind distribution
measurement in large areas
• In-field deployment
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Thank you!
Wan DU, [email protected]
Sensor placement - Constrains
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Sensor readings of T3 for 0607 and
0708
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CFD modeling - Computation
• FLUENT13.0
• k-ε turbulence model
• Two or three days per case on a server
with 12 cores and 33GB memories.
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CFD modeling - Output
• Wind vector for each grid of 5m*5m at
the height of 1.5m.
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U01
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U03
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U02
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Processes of the impact of meteorological
forcing on water
Short Wave
Long Wave
Sensible
Heat
Latent
heat
Wind
Surface Mixed Layer
Inflow
Shear
Thermocline
Hypolimnion
Outflow
Water quality studies - Model
ELCOM-CAEDYM (Estuary, Lake and Coastal Ocean
Model-Computational Aquatic Ecosystem Dynamics Model)
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Figure from http://www.cwr.uwa.edu.au
Water quality studies - Model
ELCOM-CAEDYM (Estuary, Lake and Coastal Ocean
Model-Computational Aquatic Ecosystem Dynamics Model)
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Gaussian
distribution
weak and evenly
distributed over
all directions.
Chia LS, Foong SF. 1991. Climate and weather. In The Biophysical Environment of Singapore. Chia LS, Rahman
A, Tay DBH (eds). Singapore University Press and the Geography Teachers’ Association of Singapore:
Singapore; 13–49.