Michael Zechman Database Consultant, Convertible Technology, Inc.
Emily M. Zechman Department of Civil Engineering North Carolina State University
description
Transcript of Emily M. Zechman Department of Civil Engineering North Carolina State University
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Agent-Based Modeling to Simulate Contamination Events and to Analyze
Threat Management Strategies in Water Distribution Systems
Emily M. ZechmanDepartment of Civil EngineeringNorth Carolina State University
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Contamination Event Management Strategy
• Management strategy specifies actions and reactions of decision maker– Broadcast no-drink alerts– Flush system– Isolate portion of system– Treat contaminant in-situ
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Evaluate a Management Strategy
• Maintain public health
• Maintain fire fighting flows
• Maintain flow to critical care facilities
• Avoid inciting panic due to false alarms
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Interaction of Actors during a Contamination Event
Water Distribution
System
Utility Manager
Consumers
Perpetrator
Media
Utilities Operations Staff
Public Health Services
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Agent Based Modeling Framework
• Simulate interactions of actors among one another and with water distribution system
• Predict effectiveness of alternative management strategies
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Agent Based Model
• A computer program that simulates an actor– Receives information from other agents and the
environment– Has capabilities (set of rules) to decide on an action
Agent Based Model
Environmental Information Action
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Agent Based Modeling Frameworkfor WDS Contamination Event
Decision Maker Agent
Demand
Public Broadcast
Unusual Water Quality
at Sensors
Change Hydraulics
Water Distribution
System Model
Exposure
Word-of-mouth
Consumer Agents
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Each Consumer Agent represents 10 households
2510 Residential Consumer Agents
Contaminant Source
Industrial Demand
Residential Demands (# Consumer Agents per node)
37
Illustrative Case Study
Decision Maker Agent
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Set of Scenarios
Consumer Agents modeled only
Conservative Decision Maker Agent
Adaptive Decision Maker Agent
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Scenario DrinkWord-of-
mouthBroadcast Reaction
All-Broadcast
Open hydrants
Targeted Broadcast
Add Sensors
Number Sick
Consumer Agents
Number Consumer Agents Not
Drinking
1 872 807
Decision Maker AgentConsumer Agent
-Drink water once every 4 hrs.
-Ifconsumer agent drinks waterandcontaminant concentration at node > 0 mg/L
then agent is sickened and stops drinking within 2 - 6 hours
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Contaminant Source Introduced
12:00am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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12:20am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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12:40am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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1:00am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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1:20am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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1:40am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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2:00am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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2:20am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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2:40am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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3:00am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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3:20am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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3:40am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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4:00am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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4:20am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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4:40am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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5:00am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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5:20am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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5:40am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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6:00am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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6:20am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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6:40am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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7:00am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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7:20am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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7:40am
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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8:00am
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
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SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
9:00am
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SensorSensor with unusual WQ
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
859 sick consumer agents
794 consumer agents not drinking
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
10:00am
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Scenario DrinkWord-of-
mouthBroadcast Reaction
All-Broadcast
Open hydrants
Targeted Broadcast
Add Sensors
Number Sick
Consumer Agents
Number Consumer Agents Not
Drinking
1 872 807
2 706 1246
Decision Maker AgentConsumer Agent
-If consumer agent stops drinking water, then notify another consumer agent within 10 min - 1 hr
- If agent receives word-of-mouth information, then stop drinking immediately
* Average for 5 random trials
* *
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Scenario DrinkWord-of-
mouthBroadcast Reaction
All-Broadcast
Open hydrants
Targeted Broadcast
Add Sensors
Number Sick
Consumer Agents
Number Consumer Agents Not
Drinking
1 872 807
2 706 1246
3 373 2510
Decision Maker AgentConsumer Agent
-If consumer agent receives all-broadcast, then stop drinking within 30 min – 6 hrs.
-If sensors show unusual water quality, then send no-drink broadcast to all consumer agents after 2 hrs.
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Scenario DrinkWord-of-
mouthBroadcast Reaction
All-Broadcast
Open hydrants
Targeted Broadcast
Add Sensors
Number Sick
Consumer Agents
Number Consumer Agents Not
Drinking
1 872 807
2 706 1246
3 373 2510
4 520 1056
Decision Maker AgentConsumer Agent
-If sensors show unusual water quality, then open hydrant at sensor node after 2 hrs.
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Scenario DrinkWord-of-
mouthBroadcast Reaction
All-Broadcast
Open hydrants
Targeted Broadcast
Add Sensors
Number Sick
Consumer Agents
Number Consumer Agents Not
Drinking
1 872 807
2 706 1246
3 373 2510
4 520 1056
5 339 2510
Decision Maker AgentConsumer Agent
![Page 42: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/42.jpg)
Scenario DrinkWord-of-
mouthBroadcast Reaction
All-Broadcast
Open hydrants
Targeted Broadcast
Add Sensors
Number Sick
Consumer Agents
Number Consumer Agents Not
Drinking
1 872 807
2 706 1246
3 373 2510
4 520 1056
5 339 2510
6 444 1428
Decision Maker AgentConsumer Agent
-If sensors show unusual water quality, then send targeted broadcast after 2 hrs. Reduce reaction time to 1 hr.
-If consumer agent receives all-broadcast, then stop drinking within 30 min – 6 hrs.
-If consumer agent receives targeted broadcast, then stop drinking within 10 min – 1 hr.
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Scenario DrinkWord-of-
mouthBroadcast Reaction
All-Broadcast
Open hydrants
Targeted Broadcast
Add Sensors
Number Sick
Consumer Agents
Number Consumer Agents Not
Drinking
1 872 807
2 706 1246
3 373 2510
4 520 1056
5 339 2510
6 444 1428
7 375 1437
Decision Maker AgentConsumer Agent
-If sensors show unusual water quality, then add another sensor in the near vicinity after 2 hrs. Reduce reaction time to 1 hr.
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Scenario DrinkWord-of-
mouthBroadcast Reaction
All-Broadcast
Open hydrants
Targeted Broadcast
Add Sensors
Number Sick
Consumer Agents
Number Consumer Agents Not
Drinking
1 872 807
2 706 1246
3 373 2510
4 520 1056
5 339 2510
6 444 1428
7 375 1437
8 400 900
Decision Maker AgentConsumer Agent
-If a sensor shows unusual water quality, then open a hydrant at the sensor node after 2 hrs. Reduce reaction time to 1 hr.
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Scenario DrinkWord-of-
mouthBroadcast Reaction
All-Broadcast
Open hydrants
Targeted Broadcast
Add Sensors
Number Sick
Consumer Agents
Number Consumer Agents Not
Drinking
1 872 807
2 706 1246
3 373 2510
4 520 1056
5 339 2510
6 444 1428
7 375 1437
8 400 900
9 286 1292
Decision Maker AgentConsumer Agent
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12:00am
SensorSensor with unusual WQHydrant opened
Contaminant Source Introduced
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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12:20am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
![Page 48: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/48.jpg)
12:40am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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1:00am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
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1:20am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
![Page 51: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/51.jpg)
1:40am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
![Page 52: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/52.jpg)
2:00am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
![Page 53: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/53.jpg)
2:20am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
![Page 54: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/54.jpg)
2:40am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
![Page 55: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/55.jpg)
3:00am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
![Page 56: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/56.jpg)
3:20am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
![Page 57: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/57.jpg)
3:40am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
![Page 58: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/58.jpg)
4:00am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
89
5967
13
82
34
43
37
21
![Page 59: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/59.jpg)
4:20am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
89
5967
13
82
34
43
37
21
![Page 60: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/60.jpg)
4:40am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
89
5967
13
82
34
43
37
21
![Page 61: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/61.jpg)
5:00am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
89
5967
13
82
34
43
37
21
![Page 62: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/62.jpg)
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
5:20am
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
89
5967
13
82
34
43
37
21
![Page 63: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/63.jpg)
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
5:40am
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
89
5967
13
82
34
43
37
21
![Page 64: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/64.jpg)
6:00am
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
89
5967
13
82
34
43
37
21
![Page 65: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/65.jpg)
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
7:00am
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
89
5967
13
82
34
43
37
21
![Page 66: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/66.jpg)
SensorSensor with unusual WQHydrant opened
Percentage of Sick Consumer Agents
0 – 20% 20 – 40%40 – 60%60 – 80% > 80%
8:00am
9837
54 57 7
11
76 20 34
19
13
2045
33
1
53
58
1232
48
21
89
5967
13
82
34
43
37
303 Sick Consumer Agents
1175 Consumer Agents Not Drinking
![Page 67: Emily M. Zechman Department of Civil Engineering North Carolina State University](https://reader035.fdocuments.in/reader035/viewer/2022081513/56814dbf550346895dbb18d7/html5/thumbnails/67.jpg)
Scenario DrinkWord-of-
mouthBroadcast Reaction
All-Broadcast
Open hydrants
Targeted Broadcast
Add Sensors
Number Sick
Consumer Agents
Number Consumer Agents Not
Drinking
1 872 807
2 706 1246
3 373 2510
4 520 1056
5 339 2510
6 444 1428
7 375 1437
8 400 900
9 286 1292
Decision Maker AgentConsumer Agent
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0
500
1000
1500
2000
2500
3000
1 2 3 4 5 6 7 8 9
Scenario
Num
ber
Con
sum
er A
gent
s
Sickened Consumer Agents Consumer Agents Not Drinking
Results for 9 Scenarios
5 6 7 8 9 3 4 1 2
Error bars show range of results for 5 random trials.
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Observations and Future Work
• Agent-based modeling framework provides capabilities to model interaction of actors in a water contamination event
• Additional modeling capabilities should be investigated
• Agent-based modeling framework will enable optimization of management strategies
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Acknowledgements
• This work is supported by National Science Foundation (NSF) under Grant No. CMS-0540316 under the DDDAS program.
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Management Strategies for a Contamination Event
• Strategies based on – Amount of information available– Level and credibility of t threat– Options for containment
• Open fire hydrant flush system• Open/close valves to isolate and flush contaminant• Broadcast to consumers to stop drinking• Inject chemical for in situ treatment
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2:50am
SensorSensor with unusual WQHydrant opened
Number of Sick Consumer Agents
0 - 5 5 - 1010 - 2020 - 30 > 30
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Evaluating Management Strategies
• Metrics– Measure number of sickened people– Maintain flows for fire fighting and at critical
care facilities
• Water Distribution Model to simulate contaminant plume and actions dictated by management strategy