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Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection.

Luka Grbčić1,2, Lado Kranjčević1,2, Siniša Družeta1,2

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This study introduces a new method using machine learning and optimization to pinpoint water contamination sources, their timing, and concentration. The approach effectively identifies contamination events in water distribution networks.

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Area of Science:

  • Environmental Engineering
  • Water Resource Management
  • Computational Science

Background:

  • Water distribution networks are vulnerable to contamination events.
  • Accurate identification of contamination sources, timing, and concentration is crucial for public health and effective response.
  • Existing methods may lack the precision or efficiency needed for complex networks.

Purpose of the Study:

  • To develop and evaluate a novel methodology for identifying water distribution network contamination events.
  • To determine the exact source, start and end times, and concentration of injected contaminants.
  • To compare two algorithmic frameworks based on the proposed methodology.

Main Methods:

  • Coupling a machine learning algorithm (Random Forest) for source prediction with optimization algorithms (Fireworks, MADS) for contamination parameters.
  • Developing two distinct algorithmic frameworks: one direct optimization, the other with regression prediction.
  • Testing frameworks on small and medium-sized networks with varying sensor data quality (perfect and fuzzy).

Main Results:

  • Both frameworks demonstrated robustness in accurately determining contamination source, start/end times, and concentration.
  • The second framework, utilizing Random Forest regression and MADS, showed exceptional efficiency on a network with fuzzy sensor measurements.
  • The methodology proved effective even with imperfect sensor data.

Conclusions:

  • The proposed methodology offers a reliable approach to managing water distribution network contamination events.
  • The integration of machine learning and optimization algorithms provides a powerful tool for source identification and characterization.
  • The second framework presents a highly efficient solution, particularly for real-world scenarios with noisy sensor data.