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Introduction: Emergency Management

Machine Learning Approach for Contamination Source Identification in Water Distribution Systems & Emergency Response Preparedness. Introduction: Emergency Management. Event. Prepare before an emergency to respond in a timely and effective manner during the emergency.

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Introduction: Emergency Management

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  1. Machine Learning Approach for Contamination SourceIdentification in Water Distribution Systems&Emergency Response Preparedness

  2. Introduction: Emergency Management Event Prepare before an emergency to respond in a timely and effective manner during the emergency

  3. Source Identification • Scenario • Injection location • Contaminant type • Contaminant mass • Time of year • Time of day • Injection duration Concentration t

  4. kNN Classification • Quantify uncertainties in system parameters • Perform several realizations for large number of scenarios before an emergency • Record time series for every realization • Find optimal k and similarity measure • After an emergency • Find k closest neighbors • Select most frequent scenario ?

  5. Clustering Scenarios • Clustering Model • Optimization Model • Pool of scenarios • Cluster 1 • Cluster k • Plan 1 • Plan k 5

  6. Comparison of Scenarios • Scenario Hypothesis: Optimal response for Scenario X also preforms acceptably well for Scenario Y if impact vectors are similar • Injection location • Contaminant type • Contaminant mass • Time of year • Time of day • Injection duration 6

  7. K-means Clustering • An iterative algorithm • Minimizes sum of distances for each sample to the cluster it belongs to Images from Wikipedia.com

  8. 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)

  9. Application Example Diameter (in) 10 14 24 36 9

  10. Clusters 10

  11. Application 11

  12. Application 12

  13. Application 13

  14. Summary: Integrated System • Preparedness • Response • Prepare dataset of classified time series for source ID • Set kNN algorithm parameters • Cluster scenarios based on their impact vectors • Find optimal response for cluster representatives • Collect sensor measurement time series • Find k nearest neighbors in prepared dataset • Identify the scenario • Find the cluster the scenario belongs to • Execute representative plan • Inform and run the dynamic simulation-optimization model

  15. Conclusions • Impact vector is a good measure for clustering. • Results presented as a set of clusters and representative plans are easy to interpret and use for utility operators during an emergency. Performance may be enhanced through • Using other clustering algorithms such as ISODATA. • Using other vector similarity measures.

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

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