Publications
Combining water quality and operational data for improved event detection
Water quality signals from sensors provide a snapshot of the water quality at the monitoring station at discrete sample times. These data are typically processed by event detection systems to determine the probability of a water quality event occurring at each sample time. Inherent noise in sensor data and rapid changes in water quality due to operational actions can cause false alarms in event detection systems. While the event determination can be made solely on the data from each signal at the current time step, combining data across signals and backwards in time can provide a richer set of data for event detection. Here we examine the ability of algebraic combinations and other transformations of the raw signals to further decrease false alarms. As an example, using operational events such as one or more pumps turning on or off to define a period of decreased detection sensitivity is one approach to limiting false alarms. This method is effective when lag times are known or when the sensors are co-located with the equipment causing the change. The CANARY software was used to test and demonstrate these combinatorial techniques for improving sensitivity and decreasing false alarms on both background data and data with simulated events. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. © 2012 ASCE.