Sensor Placement and Measurement of Wind for Water Quality Studies in Urban Reservoirs

1. 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

2. Large-scale and real-time water quality monitoring • Sustainable sensor network deployment. • Water quality analysis enabled by cloud computing. W10 W03 Patterns of interest Results Cloud computing W07 W05

3. Marina reservoir Marina Reservoir Kallang Basin 10% 3km 10% Marina Bay Marina Channel 2.5km

4. 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

5. Water quality studies - deployment Solar charger controller

6. Project demo video 6

7. Data collection 7

8. Data collection 8

9. 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

10. Effect of wind on water quality 10

11. Wind distribution over Marina reservoir Marina Reservoir Kallang Basin Marina Bay Marina Channel

12. Measurement of wind distribution 18750 points (20m*20m)); 6000$/ground station; 7600$/floating station; Long measurement time (at least one year) 12

13. Sensor placement and spatial prediction Where? Wind distribution with least uncertainty How? Water quality studies

14. Spatial prediction Wind?

15. Interpolation d2 Wind? d1 d3

16. Spatial correlation [Cressie,Statistics for spatial data’91; Guestrin, ICML’ 05; Krause, IPSN’ 06, 08]

17. Maximum likelihood based time series segmentation Pre-SW SW Pre-NE NE Mar.15 Dec.2 Dec.13 Jun.1 Oct.1 06-07: Mar.28 Dec.14 Dec.6 Jun.3 Sep.27 07-08: 17

18. Maximum likelihood based time series segmentation Pre-SW SW Pre-NE NE Mar.15 Dec.2 Dec.13 Jun.1 Oct.1 06-07: Mar.28 Dec.14 Dec.6 Jun.3 Sep.27 07-08: 18

19. Spatial correlation [Cressie,Statistics for spatial data’91; Guestrin, ICML’ 05; Krause, IPSN’ 06, 08] 19

20. Prior knowledge of wind distribution Atmospheric flow 20

21. Pairwise correlation learning 16 point compass rose 10 speeds (0-9m/s) Historical data of the sensor on Marina Channel

22. Sensor placement

23. Combining the results of multiple Gaussian processes • Entropy at one point: • Conditional entropy: 23

24. Sensor placement - Water quality sensitivity 24

25. Approach overview Wind distribution of the whole area (7) (3) CFD modeling Geographical information system Gaussian Regression (1) (2) Historical wind direction density Decomposed wind statistics (4) Entropy or Mutual Information Time Series Segmentation Sensor Placement Sensitivity Analysis Online temporal clustering (6) (5) Data Collection Real-time Sensor Readings Enhanced Sensor Placement

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27. Predicted wind distribution Direction Speed 27

28. Evaluation • Prediction accuracy • Interpolation • Single Gaussian model

29. 10 11 W10 W03 12 9 8 W09 13 15 7 W06 W07 14 W05 W08 19 16 6 20 W04 18 W02 1 5 17 2 W01 4 Installed Sensor (Floating or Ground) 3 Test Position

30. Average prediction error of direction

31. Average prediction error of speed

32. Prediction error VS Water quality sensitivity

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34. Water temperature • Improve the accuracy by 17% in Marina Basin

35. Conclusions Sensor placement for wind distribution measurement in large areas In-field deployment

36. Thank you! Wan DU, duwan@ntu.edu.sg

37. Sensor placement - Constrains W10 W03 W09 W06 W07 W05 W08 W04 W02 W01

38. Predicted wind distribution

39. Sensor readings of T3 for 0607 and 0708

40. CFD modeling - Computation FLUENT13.0 k-ε turbulence model Two or three days per case on a server with 12 cores and 33GB memories.

41. CFD modeling - Output Wind vector for each grid of 5m*5m at the height of 1.5m.

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43. Short Wave Long Wave Sensible Heat Latent heat Wind Outflow Inflow Surface Mixed Layer Thermocline Shear Hypolimnion Processes of the impact of meteorological forcing on water

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45. Water quality studies - Model ELCOM-CAEDYM (Estuary, Lake and Coastal Ocean Model-Computational Aquatic Ecosystem Dynamics Model) Figure from http://www.cwr.uwa.edu.au

46. Water quality studies - Model ELCOM-CAEDYM (Estuary, Lake and Coastal Ocean Model-Computational Aquatic Ecosystem Dynamics Model) 46

47. weak and evenly distributed over all directions. Gaussian distribution 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.