Skip to content

Leak Detection Project

Project: An adaptive leak detection and risk analysis framework for urban water distribution systems


Urban water distribution systems (WDS) are prone to leaks, deterioration, and contaminant intrusion. The risks associated with these conditions can be spatially diverse due to flow conditions, network characteristics, and external factors. The key to long-term sustainability of these systems is to identify high-risk regions, and to develop sound operational procedures and preventive maintenance plans. This project will develop a quantitative framework driven by a high performance simulation-optimization engine for adaptive leak detection and contaminant intrusion characterization by utilizing real time pressure, flow, and water quality data. The research will also develop a risk analysis capability that could be used to analyze economic and public health risks associated with gradual leaks and contaminant intrusion occurring during routine operations.

In a recently completed project funded by NSF, the research team has developed an adaptive high performance simulation-optimization engine and associated optimization methodologies for contaminant source identification and sampling design in water distribution systems. Building upon the understanding and experience gained from this prior research, the proposed work will investigate the following new aspects:

  • a new simulation-based leak and contaminant intrusion characterization methodology that uses routinely measured pressure, flow and water quality measurements (e.g., from a SCADA system),
  • a methodology for incorporating spatially varying macro indicators (e.g., pipe attributes) to improve the optimization search process,
  • a methodology for incorporating demand uncertainty in leak detection
  • a risk assessment methodology that targets economic losses from leaks and public health risks from contaminant intrusion.

The proposed framework could be used by decision-makers to generate and identify operational decisions and to develop long-term maintenance and expansion plans. This framework can also be used by decision-makers to evaluate the risk reduction potential of different response and maintenance actions. In collaboration with a major water utility in North Carolina, the framework developed through this research will be applied and validated using data from a mid-sized urban area.


  • develop a parallel simulation-optimization based leak detection and contaminant intrusion methodology that uses routinely measured pressure and water quality data;
  • develop a Markov Chain Monte Carlo (MCMC) methodology to incorporate prior information and demand uncertainty into the leak and contaminant intrusion detection framework;
  • develop a methodology for evaluating economic and public health risks associated with leaks and contaminant intrusion;
  • test and evaluate the computational framework and the associated components for a mid- sized urban area;
  • integrate and disseminate the research results in classroom teaching for college students, a summer program for high school students, as well as continuing education programs for practitioners.