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

introduction emergency management
Introduction: Emergency Management


Prepare before an emergency

to respond in a timely and effective manner

during the emergency

source identification
Source Identification
  • Scenario
  • Injection location
  • Contaminant type
  • Contaminant mass
  • Time of year
  • Time of day
  • Injection duration



knn classification
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


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


comparison of scenarios
Comparison of Scenarios
  • Scenario


Optimal response for Scenario X

also preforms acceptably well for Scenario Y


impact vectors are similar

  • Injection location
  • Contaminant type
  • Contaminant mass
  • Time of year
  • Time of day
  • Injection duration


k means clustering
K-means Clustering
  • An iterative algorithm
  • Minimizes sum of distances for each sample to the cluster it belongs to

Images from

virtual city of mesopolis
Virtual City of “Mesopolis”


Residential (mid density)

University Campus


Residential (mid density)


8 miles

Residential (low density)

Naval Base

Residential (low density)

application example
Application Example

Diameter (in)






summary integrated system
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


  • 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.

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