streamflow hics presentation 021610 rkm · development and operations hydrologists (doh workshop in...
TRANSCRIPT
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© 2008, Aptima, Inc. 1
www.aptima.comMA ▪ DC ▪ OH ▪ FL
© 2008, Aptima, Inc.
Accounting for Human Intervention in Streamflow Forecasting
Presentation to HICs Workshop
Silver Spring, MD
Andy Chang, Bob McCormack Lawrence Wolpert
February 16, 2010
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© 2008, Aptima, Inc. 2
Agenda
Who are we?What do we do?What’s the issue?What’s the problem?What’s the solution?What are the next steps?Questions – as if these are not enough!
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© 2008, Aptima, Inc. 3
Who are we?Aptima, Inc.
Founded in 1995; consistent annual growth (43% CAGR)Serving government and commercial clients300+ contracts with the DoDOffices– Woburn, MA, (HQ) – Washington, DC – Dayton, OH– Ft Walton Beach, FL
Interdisciplinary Small Business100+ staff (80% graduate degrees)
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What’s the issue?Streamflow Forecasting
NWS is responsible for providing weather, hydrologic, and climate forecasts and warnings – provides web-based Advanced Hydrologic Prediction Services (AHPS)
through 13 regionally-based River Forecast Centers– AHPS uses computer models that simulate river flow, rainfall, etc. to
generate predictions and these are conveyed to users
Predictions are filled with uncertainty, which is difficult to convey– Human behavior, which directly impacts the water levels and flood
conditions, remains one of the chief sources of uncertainty in hydrology forecasts
Current models do not explicitly account for behavior of the humans in the loop (forecasters, WRMs)– River regulation complicates streamflow forecasting due to lack of
human influences
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© 2008, Aptima, Inc. 5© 2008, Aptima, Inc. 5© 2008, Aptima, Inc. 5
Visual Display Design
Support EMs’ decision making regarding courses of action.
– Visualizations of river forecasts with uncertainty – Visualizations of “impact” of various flooding scenarios
Visualization of uncertainty model inputs for clear understanding of “why” predictions are what they are.
Allow for local knowledge to be incorporated into predictions
Fusion / organization of information for clear understanding of relationship to one another and enhanced orientation.
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What’s the problem?
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What’s the solution?
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ObjectivesUnderstand Influences and Actions ofWater Resource Managers Develop Models which Account for
Human Behavior
Link Hydrology ForecastsTo Human Models
Incorporate Human Behavior ForecastsInto Hydrology Predictions
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© 2008, Aptima, Inc. 9
What do we do?Human–Centered Engineering
TechnologyCapabilities
Social &Organizational
Structures
Mission, Tasks & Work
Processes
Human Agents
Congruence or ...
Disruption
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SME Interviews
Northeast RFC – Taunton, MADevelopment and Operations Hydrologists (DOH Workshop in SS)US Army Corps of Engineers – Reservoir ControlFirst Light Power Resources – New Milford, CTCalifornia-Nevada RFC – Sacramento, CA– NWS, USACE, SMUD, PG&E, SFWATER, USBR, CA Water
© 2008, Aptima, Inc. 10
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[Decision Ladders]
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© 2009, Aptima, Inc. 12
Decision Ladder Structure
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Decision Ladder Details
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Decision Ladder Details
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[Model]
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Model Approach
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Hydro Models
Data
Environmental
Human
Forecasts
Human ModelFinite State Automata
Bayesian Inference
Visualization
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© 2008, Aptima, Inc. 17
Initial Model: Finite State Automata
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Bayesian Model
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Representation of information and influencesA Bayesian Network is a directed acyclic graph with an associated set of random variables
Joint probability distribution
We use the Bayesian Network to calculate the likely actions based on the observed information
),( EVB =
))(pa,|(),,( 11
ijxXxXPxXxXP
jjii
nn
∈==∏=== K
{ }vXVv∈
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© 2006, Aptima, Inc. 19
•Prune Nodes•Adjust Conditional
Probabilities•Insert Known Prior
Distributions
Finite State Automata based on Decision Ladders
Hydro Forecasts
Integrated Model
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“Adversarial” Modeling
How do we model the actions of “non-cooperative” water resource managers, i.e., those individuals who don’t share their operational plans, schedules, etc.?The Bayesian model can learn the conditional probability distributions based on observed data.
© 2008, Aptima, Inc. 20
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Contact information
Lawrence Wolpert (PM)[email protected]
Bob McCormack (PI)[email protected]
Andy [email protected]
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[ACE]
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