Water Expert
A Decision Support Tool for Water Managers
Andrew N.S. Ernest, Ph.D., P.E., BCEE, D.WRE
Joseph L. Gutenson
Abdoul A. Oubeidillah, Ph.D.
Xiaoyin Zhang
The Environmental Institute
The University of Alabama
Jana R. Fattic, M.S., R.S.
Center for Water Resource Studies
Western Kentucky University
Lindell E. Ormsbee, Ph.D., P.E., P.H., D.WRE, F.ASCE
Water Resource Research Institute
The University of Kentucky
Thomas G. Johnson, Ph.D.
Sara Alva-Lizarraga, Ph.D.
Community Policy Analysis Center
The University of Missouri
Concept of Operations
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Heuristic Decision
Making
User-Perspective
Adaptable
Multiple Knowledge
Domains
Cross-Domain
Extensible
Cloud Deployed
Applications
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Provide Recommendations for Decontaminating Water Distribution Networks*
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Assessing the Economic Consequences of Disruptions in Water Service*
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Recommend Tools for Optimizing Distribution System Performance*
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Provide Hosted GIS and Network Visualization Tools+
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Perform Hydraulic Modeling using EPANET and extensions+
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Provide Asset Management Services^
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Recommend Best Management Practices for Natural and Urban Watersheds^
*. a. b. c. With funding from the U. S. Department of Homeland Security, Science & Technology Directorate, through The National Institute For Hometown Security
+. a. b. With funding from Caveland Environmmental Authority, supporting the development of the simplified web-based hydraulic modeling application.
^. a. b. Planned
Technology Development Process
Knowledge Engineering
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Ontologies
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Model
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Language
Taxonomy
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Tree
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Hierarchy
Semantics
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Definition
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Meaning
Taxonomic Analysis
TiO2
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More than 1600 Articles
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More than 150 Significant Keywords
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Process
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photocatalysi
s
Hit Rate Analysis
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Gap Analysis
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Taxonomic Classification
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Knowledge Extraction
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Semantic Development
monochloramin
e
viruses
activated alumina
Tacit/Operational Knowledge
Tacit
Work-Related Practical knowledge
Cognitive Task Analysis
Mind Mapping
Explicit
Literature Review
Knowledge, Skills & Abilities
Semantic Knowledge Development
Stakeholder Engagement
 Technology Review
– Gap assessment
 Tabletop Exercises
– Scenarios
– Decision Process Analysis
 Technology Deployment
– Training
– Technology validation
Rules-Based Decision Support Tool
Information
(Facts)
Inference Engine
User
Knowledge
Base
User’s Documents
Decision
Knowledge Acquisition System
Human Experts
AI
Data Mining
Inferential Logic
What hypothesis
is supported by FACTS (e.g. sensor status)
the given facts?
Backward
Chaining
Inference Engine: Software
that examines database so
as to find links between
FACTS and HYPOTHESES
Forward
Chaining
HYPOTHESIS
(e.g. incursion event)
Knowledge Development
(e.g. interview experts)
Rule Database:
Collection of unsorted /unlinked
Rules (e.g. IF_____ THEN______)
What facts
are
required to
support a
given
hypothesis?
What are the rules
being used to
support
a final conclusion
(e.g.
required facts or
hypothesis?)
Goal-Driven DSS
User
Question
Data & Facts
Model
Results
Explicit
Decisional
Response:
Predetermined
Decision Tree
Model
Results
Implicit
Decisional Response:
Traditional
Expert
System
Responses/Recommendations
Fact Sheets
Reports
WebLinks
Backward Chaining
Economic Consequence
Analysis Tool
Distribution Systems Operations
Integrative Framework
Component Interactions
Web Interface
GIS Integration
Mobile Interface
Workflow
Decontamination
Economic
Consequences
Fact Driven Inference
(Forward Chaining Inference)
Server-Side Application
(Procedural Logic)
Decon Recomendations
Decon Recomendations
Economic Consequences
JAVA Toolkit GUI
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3
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1: Question and Guidance Areas
2: Answer Area
3: Knowledge Base and Recommendation Areas
Technology Deployment
Workshop
Feedback
Demonstration
Revisions
Feedback
Implementation
Revisions
Cloud Deployment
Cross-Domain Extensibility