Smarter Water Management: A Challenge for Spatio-Temporal Network Databases (Vision Paper) KwangSoo Yang, Shashi Shekhar Jing Dai, Sambit Sahu, and Milind Naphade Department of Computer Science, University of Minnesota IBM T.J. Watson Research Hawthorne

The importance of water Water is one of our most important natural resources and water scarcity may be the most underestimated resource issue facing the world today. Source: World Meteorological Organisation (WMO), Geneva, 1996; Global Environment Outlook 2000 (GEO), UNEP, Earthscan, London, 1999. 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

Water scarcity Distribution of precipitation is varying in space and time. High rate of evaporation from surface water resource Water quality - Leakage of saline or contaminated water from the land surface - Water contamination by sewage effluent. Water quality degradation can be a major source of water scarcity. Example) Water Pollution Water scarcity is not only an issue of enough water but also of access to safe water. Developing intelligent water resource management systems is necessary to remedy the problem.

Current water management Hand grab sampling Crumbling infrastructure Leaks and theft in agriculture area Few sensors (home water meter and plant meter) Hidden water network (underground water and buried pipelines) Water Theft Water sensors 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 IBM Smarter Planet emphasizes smarter water management for planning, developing, distributing, and managing optimal use of limited water resources. Measuring, Monitoring, Modeling, and Managing Source: IBM Smarter Water Management Sensing Metering Real Time Data Integration Real Time + Historical data Data Modeling + Analytics Visualization + Decisions A lot of data are needed to fully understand, model, and predict how water flows around the planet.

Spatio-Temporal Network (STN) Water network flow is represented and analyzed as spatio-temporal network datasets. Minnesota river network source: http://geology.com/ Ground water network source: http://me.water.usgs.gov Water pipe network source: Wikipedia Spatio-temporal network databases (STNDB) will likely be a key component of smarter water management since effectiveness of decision depends on the quality of information

Challenges for STN of water networks The key issue is the quality of dataset to fully understand, model, and predict water flows in the network. 1. STN datasets not fully observable - underground natural flows - buried pipes may shift 2. Assess of STN datasets requires a novel semantics. ex) Lagrangian reference frame 3. Heterogeneity of real-world STN datasets

Hidden STN Water Network Tomography Example) Water Cooling System The internal structure and status of the cooling system may not be directly observable due to radioactive emissions from the damaged nuclear reactors. Water Cooling System / Nuclear reactor source: http://www.firstpr.com.au/jncrisis/ 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 identify monitor

Hidden STN Y = A X Water Network Tomography Y1 Y2 Y3 Y4 1 1 1 1 X1 X2 1 1 1 X1 X2 X3 X4 = Origin-destination (OD) water network flow Y = A X Input : 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 Ex) Cost matrix Flow matrix Connectivity matrix OD matrix contains several components, including origin, destination, and barrier information (e.g., max flow, amount of resistance, speed, direction) N measured data N2 hidden data Hardness under-constrained under-determined

STN Non-stationarity Water supply and consumption patterns change over time Water network supply demand Flow distribution in water networks is not i.i.d. Time varying factors Ex) Time varying water Consumption Climate change Event Geography Drought Contamination Population Temperature Leakage Factories/Farms Precipitation Disaster Pools

STN Non-stationarity Spatial location + network connectivity + time-varying property Datasets grows massively while the density of the datasets becomes sparse. Example) Water leakage detection system Historical and real-time datasets Inappropriate pressure levels and flows High risk areas in networks Burst/corroded pipes Water networks ex) sink, branch points, and water mains Monitoring Geographical information ex) home address and elevation Detecting anomalies Temporal information ex) hot moments and weather change

Complexity of STN datasets 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Inflow outflow Ex) earthquake event 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. Monitoring data Network flow Pressure Transmission time OD matrix seismogenic rupture Cost Matrix Flow Matrix Structure Matrix Detect the water leakage Recover the damaged pipe line Event data Spatial Location Temporal property The challenge here is that the anomaly detection algorithm would be intractable due to the size of the spatio-temporal network datasets.

Lagrangian reference frame 20 1 2 3 4 5 6 7 8 Time Water quality index 9 10 Measurement methods Water Quality Index : 40 (bad) – 0 (good) Eulerian : stationary point Lagrangian : moving point Moving sensor Time 1 2 3 4 Water quality index 20 30 40 40 Environmental Forensics : Where did contaminant come from ? : What are hotspots and hot moments ? Introduction to Environmental Forensics Brian L. Murphy, Robert D. Morrison 20 1 2 3 4 5 6 7 8 Time Water quality index 40 9 10

Lagrangian reference frame Access of STN datasets requires a Lagrangian frame of reference which coordinates STN datasets with STN connectivity. 1 2 3 4 5 6 7 8 Time 9 10 1 2 3 4 5 6 7 8 Time 90 9 10 1 2 3 4 5 6 7 8 Time 90 9 10 1 2 3 4 5 6 7 8 Time 9 10 (Source: http://www.sfgate.com/cgi-bin/news/oilspill/busan)

Lagrangian reference frame Need new STN database systems Example) A moving fluid (ABD) STN database systems Snapshot model Data types Storage models Access Methods Indexes Time expanded graph Queries

General frameworks to analyze STN datasets 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. Heterogeneous multi-modal networks Example) Nokia water supply contamination Water supply network Ground water network River Network Source: www.crisiscommunication.fi/files/download/ForOnline_NokiaCase.pdf Sewerage network STN data integration problem - Heterogeneous multi-modal networks - Time-varying properties - Correlation properties Population map

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.

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Source: International Water Management Institute

Spatio-Temporal Network (STN) Water network flow is represented and analyzed as spatio-temporal network datasets. Minnesota river network source: http://geology.com/ Ground water network source: http://me.water.usgs.gov Water pipe network source: Wikipedia Spatio-temporal network databases (STNDB) will likely be a key component of smarter water management since effectiveness of decision depends on the quality of information

A(t) 1. Hidden STN Time varying water network tomography Issues : X1 Y3 t1 t2 t3 t4 Time Issues : Time varying water flows Dependency of each nodes Uneven distribution of flows Data quality: missing, uncertain, unstable sample rate 3 2 3 Time dependent network flow A(t) Computationally expensive X1(t) Y3(t) How can we estimate network topology & properties ? (?)

Water Pipelines closed open open closed Flow direction Pressure Flow rate