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

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Wind distribution over Marina reservoir

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Marina Channel

Marina Bay

Kallang BasinMarina Reservoir

Measurement of wind distribution

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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]

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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)

W01

W05

W04W02

W08

W06

W09

W03 W10

W07

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

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Prediction error VS Water quality sensitivity

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W01

W05

W04W02

W08

W06

W09

W03 W10

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, duwan@ntu.edu.sg

Sensor placement - Constrains

W01

W05

W04W02

W08

W06

W09

W03 W10

W07

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.

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.