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Combining Water Quality and Operational Data for Improved Event Detection
Citation:
MURRAY, R., T. M. HAXTON, D. Hart, AND S. A. McKenna. Combining Water Quality and Operational Data for Improved Event Detection. (2010).
Impact/Purpose:
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. This paper examines the ability of algebraic combinations and other transformations of the raw signals to further decrease false alarms.
Description:
Symposium Paper