Sensor Placement and Sensor Placement and Measurement of Wind for Water Measurement of Wind for Water Quality Studies in Urban Quality Studies in Urban ReservoirsReservoirsWan 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
2
• Sustainable sensor network deployment.• Water quality analysis enabled by cloud
computing.
Patterns
of interestResults
Cloud computing
W03 W10
W05W07
Marina reservoir
3
10%
Marina Channel
Marina Bay
Kallang BasinMarina Reservoir
2.5km
3km10%
Water quality studies
4
Environmental
parameters
including wind
distribution and
water quality
Ecological model
Water quality in
the whole
reservoir
Underwater sensors, e.g., DO, Conductivity, Chlorophyll, pH, temperature
Water quality studies - deployment
Solar charger controller
Project demo video
6
Data collection
7
Data collection
8
Water quality studies
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• 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
Effect of wind on water quality
10
Wind distribution over Marina reservoir
11
Marina Channel
Marina Bay
Kallang BasinMarina Reservoir
Measurement of wind distribution
12
18750 points (20m*20m));
6000$/ground station; 7600$/floating station;
Long measurement time (at least one year)
Where?
How?
Wind distribution with least
uncertainty
Water quality studies
Sensor placement and spatial prediction
13
Spatial prediction
14
Wind?
Interpolation
15
Wind?d1
d2
d3
Spatial correlation [Cressie, Statistics for spatial data’ 91; Guestrin, ICML’ 05; Krause, IPSN’ 06, 08]
16
Maximum likelihood based time series segmentation
17
Dec.13 Mar.15
Oct.1Jun.1
NE Pre-NESWPre-SW
Dec.2
Dec.6 Mar.28
Sep.27Jun.3Dec.1406-07:07-08:
J
i
KNM
NMi
N
i
M
i
JKNM
x
x
xxxL
4
232
13
2
212
11 1
21433211
1
2
1exp
2
1
1
2
1exp
2
1
),,,|,,,,,(
Maximum likelihood based time series segmentation
18
Dec.13 Mar.15
Oct.1Jun.1
NE Pre-NESWPre-SW
Dec.2
Dec.6 Mar.28
Sep.27Jun.3Dec.1406-07:07-08:
Spatial correlation [Cressie, Statistics for spatial data’ 91; Guestrin, ICML’ 05; Krause, IPSN’ 06, 08]
19
),( jiij xxM
),( jiijij xxk
Prior knowledge of wind distribution
Atmospheric flow
20
Pairwise correlation learning
• 16 point compass rose• 10 speeds (0-9m/s)• Historical data of the sensor
on Marina Channel
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),( jiij xxM
),( jiijij xxk
Sensor placement
22
Xx
i xpxpxH )(log)()(
)2log(2
1)|( 2
|Ayi ieAyH
Combining the results of multiple Gaussian processes
• Entropy at one point:
• Conditional entropy:
3
1
*)()(j
jiji WxHxH
3
1
*)|()|(j
jiji WAyHAyH
23
Sensor placement - Water quality sensitivity
iii SxHxH *)()( iii SAxHAxH *)|()|(
24
Approach overview
25
Historical wind
direction density
Decomposed wind
statisticsSensor
Placement
Enhanced Sensor
Placement
Sensitivity Analysis
Entropy or
Mutual
Information
Time Series
Segmentation
Online temporal
clustering
Real-time Sensor
Readings
Wind distribution
of the whole area
Gaussian
Regression
Data Collection
Geographical
information
system
CFD
modeling
(1) (2)
(4)
(3)
(5)(6)
(7)
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Predicted wind distribution
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Direction Speed
Evaluation
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• Prediction accuracy– Interpolation– Single Gaussian model
2
1
34
8
7
5
62019
14
13
17
12
18
10
16
15
11
9
W01
W05
W04W02
W08
W06
W09
W03 W10
W07
Installed Sensor (Floating or Ground)Test Position
Average prediction error of direction
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Average prediction error of speed
31
Prediction error VS Water quality sensitivity
32
W01
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W04W02
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W06
W09
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W07
U01
U02
U03
Water temperature
34Improve the accuracy by 17% in Marina Basin
Conclusions
• Sensor placement for wind distribution measurement in large areas
• In-field deployment
35
Thank you!
Wan DU, [email protected]
Sensor placement - Constrains
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W09
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W07
Sensor readings of T3 for 0607 and 0708
38
CFD modeling - Computation
• FLUENT13.0 • k-ε turbulence model• Two or three days per case on a
server with 12 cores and 33GB memories.
39
CFD modeling - Output
• Wind vector for each grid of 5m*5m at the height of 1.5m.
40
W01
W05
W04W02
W08
W06
W09
W03 W10
W07
U01
U02
U03
Processes of the impact of meteorological forcing on water
Wind
Inflow OutflowSurface Mixed Layer
Long WaveShort Wave Sensible Heat Latent heat
Shear Thermocline
Hypolimnion
Water quality studies - Model
43
ELCOM-CAEDYM (Estuary, Lake and Coastal Ocean Model-
Computational Aquatic Ecosystem Dynamics Model)
Figure from http://www.cwr.uwa.edu.au
Water quality studies - Model
44
ELCOM-CAEDYM (Estuary, Lake and Coastal Ocean Model-
Computational Aquatic Ecosystem Dynamics Model)
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.
Gaussian
distribution
weak and evenly distributed over all directions.
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