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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/connectivity
Given end-to-end measurements
infer
identifymonitor
Example) Water Cooling System
Hidden STN
Water Network TomographyX1 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 Geography
Drought Contamination Population
Temperature Leakage Factories/Farms
Precipitation 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 11
12 13 14
15 16
Inflow outflow
outflow outflowseismogenicrupture
Pipes can be damaged which reduces the flow rates
Inspections of individual network components such as buried pipes are often impractical due to exceedingly large costs and time.
Network flowPressureTransmission time
OD matrix
Cost Matrix Flow MatrixStructure Matrix
Monitoring data
Event data
Spatial 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
20
1 2 3 4Time
Water quality index 30 40 40
20
1 2 3 4 5 6 7 8Time
Water quality index 20 20 20 20 20 20 20 20 20
9 10
20
1 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)
0
1 2 3 4 5 6 7 8Time
0 0 0 0 0 0 0 0 0
9 10
0
1 2 3 4 5 6 7 8Time
90 0 90 0 0 0 0 0 0
9 10
0
1 2 3 4 5 6 7 8Time
0 90 0 90 0 0 0 0 0
9 10
0
1 2 3 4 5 6 7 8Time
0 0 0 0 0 0 0 0 0
9 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 models
Mathematical 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