Pollution source localization in an urban water supply network based on dynamic water demand

Area of Science:

• Environmental Engineering
• Water Resource Management
• Network Security

Background:

• Urban water supply networks face risks from chemical and biological contamination, threatening public health and security.
• Real-time water quality monitoring using sensors is feasible, but pinpointing pollution sources remains a significant challenge.
• Existing methods often overlook the dynamic and stochastic nature of urban water demand, complicating pollution source localization.

Purpose of the Study:

• To address the complexities of pollution source localization in urban water supply networks.
• To develop an optimization algorithm that accounts for the stochastic nature of water demand.
• To accurately identify the location and concentration of pollution sources.

Main Methods:

• Modeling urban water demand using Gaussian and autoregressive models to capture its stochastic dynamics.
• Proposing an optimization algorithm to minimize the difference between analogue and detected sensor values.
• Conducting simulation experiments on two different-sized urban water supply networks.

Main Results:

• The proposed optimization algorithm effectively locates pollution sources by considering dynamic water demand.
• Simulation results demonstrate the algorithm's performance in identifying pollution source locations and concentrations.
• Comparison with the standard genetic algorithm indicates improved accuracy and robustness.

Conclusions:

• Accounting for stochastic water demand is crucial for accurate pollution source localization in water networks.
• The developed optimization algorithm offers a robust solution for real-time water quality monitoring and contamination management.
• This research contributes to enhancing the security and reliability of urban drinking water supplies.