Smarter Water Management: A Challenge for Spatio-Temporal Network Databases

(Vision Paper)

KwangSoo Yang, Shashi ShekharJing Dai, Sambit Sahu, and Milind Naphade

Department of Computer Science, University of MinnesotaIBM T.J. Watson Research Hawthorne

The importance of water

Water is one of our most important natural resources and water scarcitymay be the most underestimated resource issue facing the world today.

By 2025 about 3 billion people could face water scarcity due to the climate change, population growth, and increasing demand for water per capita. - United Nations, The Millennium Project

Source: World Meteorological Organisation (WMO), Geneva, 1996; Global Environment Outlook 2000 (GEO), UNEP, Earthscan, London, 1999.

Water scarcity is not only an issue of enough water but also of access to safe water.

Water scarcity

Developing intelligent water resource management systems is necessary to remedy the problem.

Water quality

High rate of evaporation from surface water resource

Distribution of precipitation is varying in space and time.

- Leakage of saline or contaminated water from the land surface- Water contamination by sewage effluent.

Example) Water Pollution

Hand grab sampling

Current water management

Water Theft

Water sensors

• Crumbling infrastructure

• Leaks and theft in agriculture area

• Few sensors (home water meter and plant meter)

• Hidden water network (underground water and buried pipelines)

One technology to remedy this problem is to implement fully integrated systems which allow monitoring, analyzing, controlling, and optimizing of all aspects of water flows.

Smarter Water Management

Source: IBM Smarter Water Management

IBM Smarter Planet emphasizes smarter water management for planning, developing, distributing, and managing optimal use of limited water resources.

A lot of data are needed to fully understand, model, and predict how water flows around the planet.

Sensing Metering

Real TimeData Integration

Real Time+ Historical data

Data Modeling+ Analytics

Visualization+ Decisions

Measuring, Monitoring, Modeling, and Managing

Spatio-Temporal Network (STN)

Water network flow is represented and analyzed as spatio-temporal network datasets.

Minnesota river networksource: http://geology.com/

Water pipe network source: Wikipedia

Spatio-temporal network databases (STNDB) will likely be a key component ofsmarter water management since effectiveness of decision depends on the quality of information

Ground water networksource: http://me.water.usgs.gov

Challenges for STN of water networks

1. STN datasets not fully observable - underground natural flows - buried pipes may shift

3. Heterogeneity of real-world STN datasets

2. Assess of STN datasets requires a novel semantics. ex) Lagrangian reference frame

The key issue is the quality of dataset to fully understand, model, and predict water flows in the network.

Hidden STN

Water Cooling System / Nuclear reactorsource: http://www.firstpr.com.au/jncrisis/

Water Network Tomography

The internal structure and status of the cooling system may not be directly observable due to radioactive emissions from the damaged nuclear reactors.

Network tomography is one solution to understand the internal characteristic of the network using end-to-end measurements, without needing the cooperation of internal nodes.

water loss

flow delay or bottleneck

topology/connectivityGiven end-to-end measurements

infer

identifymonitor

Example) Water Cooling System

Hidden STNWater Network Tomography

X1 X2

X3 X4

Y1

Y2

Y3 Y4

Y1

Y2

Y3

Y4

=

X1

X2

X3

X4

1

0

1

0

1

0

0

0

0

1

1

0

0

1

0

1

Y = A XInput : Given nodes X and Y,

flows at nodes Y Output : Estimate A

Objective : Minimize the error

Constraints : Incorporate the temporal and spatial dependence: Sparse matrix : Tracer data

Origin-destination (OD) water network flow

OD matrix contains several components, including origin, destination, and barrier information (e.g., max flow, amount of resistance, speed, direction)

Ex) Cost matrix Flow matrix Connectivity matrix

Hardness • under-constrained• under-determined

N measured data N2 hidden data

STN Non-stationarity

Waternetwork supply demand

Climate change Event GeographyDrought Contamination Population

Temperature Leakage Factories/FarmsPrecipitation Disaster Pools

Water supply and consumption patterns change over time

Time varying factors

Flow distribution in water networks is not i.i.d.

Ex) Time varying water Consumption

STN Non-stationarity

Spatial location + network connectivity + time-varying property

Datasets grows massively while the density of the datasets becomes sparse.

Temporal informationex) hot moments and weather change

Water networksex) sink, branch points, and water mains

Geographical information ex) home address and elevation

Historical and real-time datasets • Inappropriate pressure levels and flows • High risk areas in networks• Burst/corroded pipes

Example) Water leakage detection system

Monitoring

Detectinganomalies

Complexity of STN datasets

1 2 3

4 5

6 7 8 9

10 11 12

7

1 23 4

56

8

9 10 1112 13 14

15 16

Inflow outflow

outflow outflowseismogenicrupture

Pipes can be damaged which reduces the flow ratesInspections of individual network components such as buried pipes are often impractical due to exceedingly large costs and time.

Network flowPressureTransmission time

OD matrixCost Matrix Flow MatrixStructure Matrix

Monitoring data

Event dataSpatial Location Temporal property

Detect the water leakageRecover the damaged pipe line

The challenge here is that the anomaly detection algorithm would be intractable due to the size of the spatio-temporal network datasets.

Ex) earthquake event

Water Quality Index : 40 (bad) – 0 (good)

Lagrangian reference frame

Eulerian : stationary point Lagrangian : moving point

Measurement methods

Moving sensor

201 2 3 4Time

Water quality index 30 40 40

201 2 3 4 5 6 7 8Time

Water quality index 20 20 20 20 20 20 20 20 20

9 10

201 2 3 4 5 6 7 8Time

Water quality index 40 20 40 20 20 40 20 40 40

9 10

Environmental Forensics: Where did contaminant come from ?: What are hotspots and hot moments ?

Introduction to Environmental ForensicsBrian L. Murphy, Robert D. Morrison

Lagrangian reference frame

(Source: http://www.sfgate.com/cgi-bin/news/oilspill/busan)

01 2 3 4 5 6 7 8Time

0 0 0 0 0 0 0 0 09 10

01 2 3 4 5 6 7 8Time

90 0 90 0 0 0 0 0 09 10

01 2 3 4 5 6 7 8Time

0 90 0 90 0 0 0 0 09 10

01 2 3 4 5 6 7 8Time

0 0 0 0 0 0 0 0 09 10

Access of STN datasets requires a Lagrangian frame of reference which coordinates STN datasets with STN connectivity.

Lagrangian reference frame

Example) A moving fluid (ABD)

Snapshot model

Time expanded graph

Data types

Indexes

Access Methods

Queries

Storage models

STN database systems

Need new STN database systems

General frameworks to analyze STN datasets

Source: www.crisiscommunication.fi/files/download/ForOnline_NokiaCase.pdf

Example) Nokia water supply contamination

- STN models for complex real-world networks- Novel network analysis modelsMathematical approach - Real-world network model is unknown or too complex to be mathematically described.

Water supply network

Ground water network

River Network

Sewerage network

Heterogeneous multi-modal networks

Population map

STN data integration problem - Heterogeneous multi-modal networks - Time-varying properties - Correlation properties

Conclusion

Water resource management is one of most important part for our survival.

We need a Spatio-temporal network databases and analysis tools to monitoring, analyzing, controlling, and optimizing of all aspects of water flows.

We poses 3 main challenges to handle spatio-temporal network datasets for water flows.

Acknowledgement

This work was supported by NSF and USDOD.

References

1. Water: How can everyone have sufficient clean water without conflict?(2010), United Nations, http://goo.gl/pdsr0, Retrieved Mar 17, 2011. 2. Smarter planet, Wikipedia, http://goo.gl/ay5W8, Retrieved Mar 17, 2011. 3. Water Management, Wikipedia, http://goo.gl/aNllg, Retrieved Mar 17, 2011. 4. Let’s Build a Smarter Planet: Smarter Water Management, Dr. Cameron Brooks, Sep 22, 2010, IBM, http://goo.gl/XvCIB 5. IBM Smarter Water Keynote(2010), IBM, http://goo.gl/4zRMw 6. Water supply network, Wikipedia, http://goo.gl/cF4IG, Retrieved Mar 17, 2011. 7. Nokia water supply contamination, Wikipedia, http://goo.gl/fDfVx, Retrieved May 30, 2011. 8. Batchelor, G.: An introduction to fluid dynamics. Cambridge Univ Pr (2000) 9. Chartres, C., Varma, S.: Out of Water: From Abundance to Scarcity and How to Solve the World’s Water Problems. Ft Pr (2010)10. Chen, F., et al.: Activity analysis based on low sample rate smart meters. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining(to appear). ACM (2011)11. Dai, J., Chen, F., Sahu, S., Naphade, M.: Regional behavior change detection via local spatial scan. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. pp. 490–493. ACM (2010)12. Marien, M.: Jc glenn, tj gordon and e. florescu, 2010 state of the future, the millennium project, washington, http://www.stateofthefuture.org. Futures (2010)13. Molden, D.: Water for food, water for life: a comprehensive assessment of water management in agriculture. Earthscan/James & James (2007)14. Perry, W.: Grand challenges for engineering. Engineering (2008)15. Vardi, Y.: Network Tomography: Estimating Source-Destination Traffic Intensities from Link Data. Journal of the American Statistical Association 91(433) (1996)

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Water Pipelines

Flow direction Pressure

Flow rate