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An entropy-based approach to detect critical nodes for modelling future demand uncertainty in Water Distribution
R Magini1, E Ridolfi1, J Marques2
1Sapienza, University of Rome, Department of Civil, Environmental and Building Engineering, Rome, Italy.
Water Research
|July 23, 2026
Summary
This study introduces an entropy-based method to identify critical nodes in water distribution networks (WDNs). This approach enhances robust network design by quantifying demand uncertainty and its impact on system efficiency.
Area of Science:
- Civil Engineering
- Environmental Engineering
- Water Resource Management
Background:
- Urban water distribution networks (WDNs) face challenges in predicting future water demand, impacting network efficiency and design.
- Current design methods often oversimplify demand distribution, concentrating it at specific nodes, which can lead to inefficiencies under varying loads.
- Accurate identification of critical demand nodes is crucial for developing resilient WDNs capable of handling future uncertainties.
Purpose of the Study:
- To develop an entropy-based method for identifying critical nodes in WDNs.
- To quantify the uncertainty associated with future water demand and its impact on network performance.
- To inform robust design optimization of WDNs by considering future demand scenarios.
Main Methods:
- Utilized an entropy-based approach to quantify uncertainty in nodal water demand.
- Performed global sensitivity analysis using Direct Information Transfer (DIT) to identify critical nodes.
- Developed a multi-objective robust optimization (RO) model incorporating future demand uncertainty and statistical variations.
Main Results:
- The entropy-based method effectively identified critical demand nodes within the WDN.
- Sensitivity indices quantified the impact of nodal demand variations on the overall system response.
- The RO model successfully integrated future demand scenarios, including overloaded critical nodes and user behavior uncertainty.
Conclusions:
- The proposed entropy-based method provides a robust framework for identifying critical nodes in WDNs, crucial for effective design.
- Quantifying demand uncertainty through sensitivity analysis enables more resilient network planning.
- The developed RO model offers a practical approach to designing WDNs that can adapt to future uncertainties and optimize performance.
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