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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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A Benchmark for Physics-informed Machine Learning of Chlorine Concentration States in Water Distribution Networks.

Luca Hermes1, André Artelt1,2, Stelios G Vrachimis2

  • 1Bielefeld University, Inspiration 1, 33615 Bielefeld, NRW Germany.

SN Computer Science
|June 9, 2025
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Summary

This study introduces a new benchmark dataset for estimating chlorine levels in water distribution networks (WDNs). It also presents two neural network models to help ensure safe drinking water quality.

Keywords:
BenchmarkChlorine state estimationDeep learningGraph Neural NetworksRecurrent Neural NetworksSurrogate modelsWater distribution networksWater quality

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Area of Science:

  • Environmental Engineering
  • Water Treatment Technologies
  • Computational Fluid Dynamics

Background:

  • Maintaining high-quality drinking water is crucial for public health, with chlorine disinfection being a standard practice in water utilities.
  • Accurate monitoring of chlorine concentration in dynamic water distribution networks (WDNs) is essential for ensuring water safety and effective disinfection.
  • Existing methods for chlorine estimation in WDNs face challenges due to the complexity and dynamic nature of these systems.

Purpose of the Study:

  • To develop a comprehensive benchmark dataset for training and evaluating chlorine concentration estimation methods in WDNs.
  • To introduce and assess novel neural surrogate models for real-time chlorine state estimation.
  • To provide baseline performance evaluations for advanced chlorine monitoring techniques in WDNs.

Main Methods:

  • Creation of a diverse benchmark dataset comprising 18,000 scenarios across 'Hanoi', 'Net1', and 'CY-DBP' water networks.
  • Inclusion of varied chlorine injection patterns to simulate realistic physical dynamics within WDNs.
  • Development and evaluation of two neural surrogate models: a physics-informed Graph Neural Network (GNN) and a physics-guided Recurrent Neural Network (RNN).

Main Results:

  • The benchmark dataset effectively captures diverse operational conditions and physical dynamics relevant to chlorine transport in WDNs.
  • The proposed GNN and RNN models demonstrate promising capabilities for accurate chlorine state estimation.
  • Baseline performance metrics established for the GNN and RNN provide a foundation for future research in WDN chlorine monitoring.

Conclusions:

  • The developed benchmark is a valuable resource for advancing research in chlorine concentration estimation methodologies for WDNs.
  • Physics-informed and physics-guided neural networks offer a viable approach for accurate and efficient chlorine state estimation.
  • This work contributes to ensuring safer drinking water by improving the tools for monitoring disinfectant levels in distribution systems.