Lindell, research partners developing
new water utility emergency plans

A professor of urban planning at Texas A&M is partnering with civil engineering and public service scholars to research the development of emergency procedures for water utility operators.

For the two-year, $377,000 National Science Foundation project, Michael K. Lindell is teaming with Emily Zechman, assistant professor of environmental and water resources engineering, Kelly Brumbelow, associate professor of water resources engineering, and Jeryl Mumpower, professor of government and public service at Texas A&M.

“In the event that a contaminant is introduced to a water distribution system, utility operators must respond quickly to protect public health, while maintaining water availability for fire-fighting, minimizing unnecessary economic losses, and avoiding false alarms,” wrote the researchers in an abstract of the project, “An Agent-based Modeling Framework for Response Planning to Contamination Events for Water Utilities.”

Response planning, they added, can significantly reduce risk for water utilities and is an important step in protecting public health.

Plans of action for real-time utility response are difficult to design, they wrote, based on the range of uncertainty and variability in the location, time, type and duration of contaminant.

“Moreover,” they wrote, “knowledge of how water utility consumers respond to emergency situations is very limited.”

The researchers are planning to use agent-based models (ABM) to simulate the interactions of utility operators, consumers and public health agencies and their impact on the propagation of the contaminant.

An ABM is a computational model for simulating the actions and interactions of autonomous individuals with a view to assessing their effects on the system as a whole. Such a model can simulate the simultaneous operations of multiple agents, in an attempt to re-create and predict the actions of complex phenomena.

From the ABM-generated simulation, the researchers will employ optimization methods to identify general rules for choosing response options, probabilistic outcomes of decisions, and an analysis of the residual uncertainty associated with each decision.

“Optimization methods will investigate new approaches for identifying dynamic action plans that incorporate uncertain information and changing conditions,” wrote the researchers.

- Posted: Oct. 7, 2009 -