IBM Research ‘Big Bets’
in Sustainable Technologies:
Smarter Water
Management
Sherif El-Rafei,
Business Development Executive, IBM
Research Middle East & Africa
srafei@eg.ibm.com
April 2013
© 2013 IBM Corporation
Smarter Planet/Smarter City
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© 2013 IBM Corporation
Reimagining how science and technology can have
impact
• Managing human
impact on rivers by
streaming
information
• Reimagining the
energy grid by
synchronizing supply
• Fighting infectious
disease by spreading
data
• Reducing traffic jams
by creating them
• Reducing CO2 while
boosting business
efficiency
• Improving
communication by
talking to the Web
• Helping premature
infants by sensing
complications before
they happen
• Mapping beneath the
seafloor to help
reduce the risk of dry
holes
• Creating drinking
water by filtering
oceans
© 2013 IBM Corporation
Smarter Water Management Overview
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© 2013 IBM Corporation
Smarter Water Management means enabling higher levels collaboration
and innovation across value chains and ecosystems
Environment
Regulation
Supply
Climate Change
Demand
Control
Engagement
Intelligence
Infrastructure
Recycled/Treated
Natural
Water
Sources
Raw
Water
Transport
Clean
Water
Supply
Consumers
Sewage
Treatment
© 2013 IBM Corporation
We Work at Three “Scales”
Natural scale
 Water resource mapping
and availability
 Water quality monitoring
and management
(surface and subsurface)
 Land use analysis
 Extraction monitoring
(surface and subsurface)
 Flood control
Utility scale
Water quality and usage
Discharge, combined
sewer overflow
Asset management
“Smart levees” and
levee monitoring
systems
Weather event
assimilation
Energy management
Enterprise Scale
 Water usage tracking
 Water quality control
(into and within plants,
discharges)
 Supply chain
optimization
 Energy management
 Business process
improvements
 Metrics and
management
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Smarter Water Source Management
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Hydrogeosphere – an Integrated Computational Modeling Framework
Water Cycle
Hydrological model Water Cycle
Watson hydrological model
Deep Thunder
Ocean Model
Groundwater model
basin model
Weather /
Climate /
Atmospheric
modeling
New Insights come from integration
of multiple disciplines
Water Quality (Measurement Large River Basin Simulation
Management Technology)
© 2013 IBM Corporation
Phase II - Large River Basin Simulation
 Cooperation between IBM Austin Research Laboratory & University of
Texas.
 Full scale simulation of the Guadalupe River.
– Demonstrating a predictive
model with ~100X speedup.

Width of each segment represents depth

The color represents flow velocity

Red: high velocity

Blue: low velocity
 Availability of geographical and
sensor data is crucial to success.
 Eventual goal: Mississippi River.
– About 80X larger than the Guadalupe.
 Total number of reaches: ~3,900
 Number of pour ports: ~1,800
 Total length: ~15,000 km
 Modeled by 131K nodes, two unknowns at each
node (depth and velocity).
262K unknowns solved at each time point
© 2013 IBM Corporation
Subsurface (Hydro-geological) Flow Model
 Variable-scale using unstructured (tetrahedral)
meshes
 Time-dependent, model-based subsurface flow
modeling
Can be coupled with the surface flow model
Model solved using:
 Locally conservative multiphase (water, air)
 Numerical model based on Control-Volume Finite
Element discretization
 Can include geo-mechanical effects of
elastic/plastic aquifers, and topography and
density driven flows
 Transient temperature effects, fracture and faults
can be specified
 Numerical kernel extensively used in basin
modeling (scalable to from millions to billions of
cells)
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5-6 April 2010 Flooding Event
 Coastal storm with heavy rains (up to 284mm in 24 hours)
starting at about 1700 BRT on 5 April 2010 – heaviest
recorded compared to the previous 48 years
 One of the most significant global weather events of 2010
 Local flooding leading to mudslides, killed over 200 people
and left 15000 homeless
 Widespread disruption of transportation systems (e.g., road
closures, airport and rail delays)
 Rio de Janeiro mayor Eduardo Paes admitted that the city's
preparedness for heavy rainfall had been "less than zero,"
but added "there isn’t a city that wouldn’t have had
problems with this level of rainfall."
© 2013 IBM Corporation
What is Weather Modelling?
A mathematical model
that describes the
physics of the
atmosphere
–The sun adds energy, gases rise
from the surface, convection
causes winds
Numerical weather
prediction is done by
solving the equations of
these models on a 4dimensional grid (e.g.,
latitude, longitude,
altitude, time)
Complementary to
observations (e.g., NWS
weather stations)
Solution yields predictions of surface and
upper air
–Temperature, humidity, moisture
–Wind speed and direction
–Cloud cover and visibility
–Precipitation type and intensity
© 2013 IBM Corporation
Match the Scale of the Weather Model with the Client’s Needs
2km
2km
 “You don't get points for predicting rain. You get points for
building arks.” (Lou Gerstner)
Capture the geographic characteristics that affect
weather (horizontally, vertically, temporally)
Ensure that the weather forecasts address the
features that matter to the business
© 2013 IBM Corporation
Short-Term Weather Event Prediction and Observation
Nowcasting (Sensors)
Forecasting (Modelling)
NWS / Commercial
Providers
Deep Thunder
Forecast for longer-term
planning where decisions
require days of lead time,
but may not have direct
coupling to business
processes
Forecast for asset-based
decisions to manage
weather event, pre-stage
resources and labor
proactively
Continental to
Global Scale
72-168
Remote
Fine-tune
approach based
upon
extrapolation
from Doppler
radar and
satellite
observations
Local Scale
18-72
In Situ
Near-real time revision
Local Scale
3
0
Time Horizon for a Local Weather Event (Hours of Lead Time)
© 2013 IBM Corporation
Command Center for Rio de Janeiro
© 2013 IBM Corporation
The Importance of Real-Time Coastal Awareness
Protecting coastal cities
Protecting our environment
Monitoring/managing coastal
agriculture and industries
Our vision: coastal awareness,
weather prediction and flood prediction in concert
to protect citizens, infrastructure, and the environment
Managing maritime
operations
Tracking pollutant
dispersion
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© 2013 IBM Corporation
Realtime Coastal Awareness
• Collaboration with National University of Ireland, Galway
• Objective: Real-time prediction of bay conditions (quality
and circulation patterns) for environmental decision support
• Challenges:
– Noise and uncertainty in measurements
– Model scale
• Methodology:
– Data assimilation for real-time modelling
– HPC implementation
• CODAR = high frequency radar for water surface speed
© 2013 IBM Corporation
CODAR project infrastructure
CODAR
CODAR
`Assimilation of 10GB / hr.
HF radar for water
speed
• CODAR adds to wealth of sensors in Galway Bay
– Smart Bay tidal gauges and flow measurements
– Sonars for water velocity at varying depth
– Two weather stations
• Ideal prototyping environment
© 2013 IBM Corporation
Smarter Water Distribution
Management
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A Measurement and Modeling Technology Platform
Measurement Platform
Management Environment
Smart Sensor Bus
Integrated Modeling Environment
General Technology platform to deliver physical intelligence for smarter
planet applications by leveraging state of-the-art metrology, a broad set of
models and unique controls to different length & time scales of the physical
world
© 2013 IBM Corporation
Leakage & Pipeline Failures…
 Water losses reduction
– More than 32 billion cubic meters of treated
–
–
water is lost annually through distribution
network leaks [1]
A conservative estimate of the total annual cost
of water loss to utilities worldwide is US$14
billion [1]
According to IWA, 15%~30% water is leaked [2]
–15%~30% water leaking in the world[2]
– 900 leakage/burst per year in big cities[4]
 Public image improvement
– 250~300 pipe bursts per year in Trondheim City,
–
Norway [3]
About 900 leakage per year in Hong Kong. [4]
May 25, 2010, pipe burst at Beijing
JingGuang Bridge causing a 5-hour water
supply disruption and severe traffic jam in
the business center
Source:
1)From Bentley company
2) “Water Industry: Managing Leakage”. Engineering and Operations Committee, UK.
3)Jianhua Lei and Sveinung Segrov, Statical approach for describing failures and lifetimes of water mains. Wat. Sci. Tech. Vol. 38, No. 6, pp. 209-217.
4) Hong Kong Water Supplies Department Annual Report (2008)
5) A Lambert, (2001) What do we know about pressure-leakage relationships in distribution systems? IWA Conf. n Systems approach to leakage
control and water distribution system management. Brno, Czechoslovakia. ISBN 80-7204-197-5
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© 2013 IBM Corporation
Addressing Non-Revenue Water using Analytics and Optimization
Leakage or Theft Detection at the
Residential Level
Understand usage patterns and detect
anomalies for low and high consumption
to detect leakage, theft or faulty meters
Leakage Detection at the Network Level
using optimization
Find “optimal” location of leak(s) to explain
difference between actual measurements
and model predicted measurements
Leakage Reduction using Dynamic
Pressure Control
Optimal Valve Placement for
Pressure Reduction
Create optimization model to adjust the
pressure dynamically so that only the
required flow will be supplied yielding cost
reduction in energy and water achieved.
Use an optimization model to find the
optimal number of valves, and their
location, so as to enable the most effective
pressure management
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© 2013 IBM Corporation
© 2013 IBM Corporation
Business
Innovation
Architecture
Demo
Asset Lifecycle planning enables informed operational and strategic decision
support
annual cost
Usage / Smart
Meters
Infrastructure
Network
Relationships
Failure
History
Asset
Condition
Assessment
Risk
Estimation &
Prediction
Environmental
Attributes
replace
repair
Capital
Budget
failure rate
{Labor, material, service
interruptions, …}
Replacement
Cost
Estimation
Strategic
Plan
{Labor, routine disruptions,
cost, material, ….}
Maintenance
Cost
Estimation
Spatial
Coordinates
Asset
Attributes
Periodic inspection
Business
Constraints
Failure
Impact
Decision
Support
Operationa
l Plan
Operational
Budget
Backup
Assets
Strategic replacement in 2, 5, and 10 years
Efficient use of crew and equipment
© 2013 IBM Corporation
Integrated Water Management
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Strategic Water Information Management Platform
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Water Resource Management
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© 2013 IBM Corporation
Strategic Water Information Management (SWIM)
Platform
Data types (as examples)
(from multiple sources and systems)
Run-off
Visualization layer
Quantity/Flo
w
Applications layer
(Open) standards
Business rules layer
Security
Usage and
Discharge
“An integrated set of
technologies, data and tools”
Quality
Environment/
Ecology
Models layer
Data handling layer
Climate
Economic
Data content layer
Geology/
hydrology
Network layer
Energy data
Sensing layer
© 2013 IBM Corporation
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© 2013 IBM Corporation
Hindi
Ευχαριστώ
Thank You
English
Greek
Dziekuje
Polish
‫شكرا‬
Russian
Thai
Portuguese
Arabic
Gracias
Merci
Spanish
Obrigado
Teşekkür ederim
Turkish
French
Mulţumesc
Grazie
Italian
Tamil
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Februar
Romanian
Simplified Chinese
Traditional Chinese
Danke
Japanese
German
© 2013 IBM Corporation
Environmental Analytics Platform
Vineyard
Factories, Bridges,
Refineries, Airports etc.
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© 2013 IBM Corporation
Low-Power Mote Technology (LMT)
 LMT—a wireless data gathering
technology
 A general IBM wireless sensor
platform
–
–
Highly robust and scalable sensing solution
Forms Mesh Network
 World’s lowest power
consumption
–
5 to 7 year lifetime with two AA batteries
 Very flexible and modular design
What are the benefits ?
 Means to maintain soil moisture while
minimizing water usage for irrigation
 Prevent frost and/or fungal damage
 Alarm workers to take measures to save
crops.
 Predicting local frost damage
 Determine optimum harvest point
 Optimize crop growing and food processing
 Sensors can be located with +/- 3  Improved asset and operational management
feet
 Environmental sensing:
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–
–
–
–
–
–
–
–
–
Temperature and Humidity
Soil Moisture and Temperature
Sun light / irradiation
Dew point
Pressure, Air flow
Carbon dioxide
Presence and Occupancy
Corrosion and Air quality
Location
© 2013 IBM Corporation
LMT for Agriculture Applications
What can we monitor ?
• Soil temperature
• Soil moisture
• Air temperature
• Humidity
• Sunlight
• …..
• pH ?
• What would like to measure which you cannot
do today ?
What are the benefits ?
• Means to maintain soil moisture while minimizing
water usage for irrigation
• Predicting local frost damage
• Alarm workers to take measures to save crops.
• Determine optimum harvest point
• Prevent frost and/or fungal damage
• Optimize crop growing and food processing
• Improved asset and operational management
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© 2013 IBM Corporation
Soil Moisture Detection – Full field and largescale IR imaging
Semi-spherical mirror
IR camera
Less moisture
[1] Data from Iven Mareels’ IBM presentation in January 2011
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© 2013 IBM Corporation
Example – Crop Growing
• Total of 35.3 acres over three fields
in Eastern New York
• 95 motes supporting 475 sensors
•
•
•
•
•
Soil temperature
Air temperature
Soil moisture
Humidity
Light
• Data streamed back into a central
gateway every 2 s
• Software Solution allows remote
monitoring and control
• Deep Analytics
•
•
•
•
•
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Moisture Modeling
Time Series Forecasting
Optimization
Statisical Correlation
….
© 2013 IBM Corporation
Example - Fungal Disease Detection
• Phytophthora is a fungal disease in potatoes, which depends on temperature,
humidity and whether the leaves are wet.
• Extensive wireless sensing system in the Netherland measures air pressure,
temperature, relative humidity and illumination
• System alerts farmers of patches within his fields which are most susceptible
and can be used to gauge the steps that need to be taken.
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Research’s Strategic Disciplines
Exploratory
Business
Analytics &
Math. Sciences
Industry
Solutions
Services
Software
Systems
Technology
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