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Area of Science:

  • Environmental Engineering
  • Data Science
  • Water Resource Management

Background:

  • Water supply contamination poses significant public health risks.
  • Accurate identification of pollution sources is crucial for effective mitigation.
  • Existing methods may lack the speed and precision required for real-time response.

Purpose of the Study:

  • To develop and validate a novel machine learning algorithm for water supply pollution source identification.
  • To determine key contamination event variables including start time, end time, and chemical concentration.
  • To optimize the algorithm for high-performance parallel systems.

Main Methods:

  • Utilized a hybrid approach combining Artificial Neural Networks (ANNs) for classification and Random Forests (RF) for regression.
  • Performed parallel Monte Carlo water quality and hydraulic simulations.
  • Employed sensor data within a water supply network and a tournament-style selection for source identification.

Main Results:

  • Achieved 100% accuracy in identifying the true pollution source on a small network (92 nodes).
  • Demonstrated high accuracy (29/30 runs) on a medium-sized network (865 nodes).
  • Provided accurate estimations for contamination event start/end times and concentrations with low root mean square errors.

Conclusions:

  • The novel algorithmic framework effectively identifies pollution sources in water supply systems.
  • The algorithm accurately determines critical contamination event parameters.
  • This approach offers a robust and efficient solution for water security and management.