Adaptive Emergency Response to Water Distribution System Contamination Events

1. Adaptive Emergency Response to Water Distribution System Contamination Events

2. Introduction: Emergency Management Event

3. Research Objective Developing a computation model that provides timely and effective response recommendations in a time-varying emergency environment.

4. Time-varying Environment • Why? • Response delay increases • “Perceived” scenario changes over time • Managers execute response actions • Consumers change their behavior Health impacts Response decisions 4

5. Model Development • Contamination Scenario • Managers • Consumers • Analysis • Injection location • Contaminant type • Contaminant mass • Time of year • Time of day • Injection duration • Response mechanisms: • hydrant operation • food-grade dye injection • Decisions: • When to respond • How to respond • Drink water 5 times a day • Stop drinking and change demand if they get sick or are alarmed • Health impact is ultimate total ingested mass of contaminant • Hydraulic and quality analysis by EPANET • Simulation considers: • time is passing • , • perceived scenario changes • , • actions are taken Scenario is an input Perceived scenario is time-varying

6. Dynamic Optimization Problem • Decision variables: • Elements of response protocol • Hydrant operation • Food-grade dye injection • Objective function: • Minimization of health impacts (without loss of generality)

7. Evolutionary Dynamic Optimization Algorithms • Multi-objectivization • Objective functions: • Minimization of health impacts • Maximization of diversity in response protocols Boost diversity after a change: the EA is initially run in standard fashion. As soon as a change in the environment is identified, explicit strategies are implemented to generate diversity in the population: hypermutation(Cobb 1990) Maintain diversity throughout the run: convergence is limited through constant diversification hoping that a diverse population is more promising to adapt to time-varying changes: Random immigrants method (Grefenstette 1992); thermodynamic genetic algorithm (Mori et al. 1997); multiobjective-based method (Bui et al. 2005). Memorize good solutions:the algorithm retains good solutions from past generations. This strategy provides diversity and helps the algorithm retrieve the optimum in repetitive environments: Diploidy approach (Goldberg and Smith 1987). Use multiple subpopulations: the population is clustered into multiple subpopulations that evolve together to explore multiple promising regions of the decision making space: multinational GA (Ursem 2000); self-organizing scouts (Branke et al. 2000); the shifting balance approach (Wineberg and Oppacher 2000).

8. Multi-objectivization Helpful for Exploration Diversity Helpful for Exploitation Useless! Health Impacts

9. Diversity 3 4 1 2 Plan 1 1 4 3 2 Plan 2

10. Diversity • Distance from the nearest solution • Distance from the best solution • Average distance from all solutions

11. Virtual City of “Mesopolis” Airport Residential (mid density) University Campus Downtown Residential (mid density) Industry 8 miles Residential (low density) Naval Base Residential (low density)

12. Application Example Diameter (in) 10 14 24 36 True Scenario Perceived Scenario 12

13. Process: Effect of Response Delay No response Current Optimum Current Optimum

14. Process: Effect of All Factors True scenario identified Best current plan executed Consumers react Superposition

15. Summary and Conclusions • Dynamic simulation-optimization provides timely and realistic response recommendations through adapting to the varying emergency environment. • The model converges to a moving global optimal response protocol over time. Performance may be enhanced through • more realistic simulation. • providing dynamic model with good start solutions.

16. Acknowledgement This research is supported by the National Science Foundation under Grant No. CMMI-0927739.

18. Different Diversity Measures

19. Different Response Mechanisms

20. Different Factors