The design of an efficient online water quality monitoring
approach is complicated by several factors. First, changes
within the system including variation of source water or
the treatment process, the fluctuation of water demand,
and changes in the water quality sensors can create anomalous
water quality signatures, different from contamination
events. Distinguishing normal behavior from abnormal
behavior is difficult in these situations. Second, due to the
complex characteristics of water quality, a physical model
describing water quality behavior is difficult to derive and
implement in real time. This complexity has motivated the
extensive use of data-driven models for predicting water
quality behavior. Although data-driven approaches have
proven to be an efficient alternative, several challenges persist
including: accurate detection of contamination events
with low false positive rates (FPRs) and low detection
latency; definition of an adequate trade-off between sensitivity
to contamination events and remaining insensitive to
non-event changes; characterization of faults due to water
quality sensor failures, numerical algorithmic instabilities
and water process operation. In an attempt to alleviate
some of these challenges, research in both forecasting and
monitoring has been conducted.
The design of an efficient online water quality monitoringapproach is complicated by several factors. First, changeswithin the system including variation of source water orthe treatment process, the fluctuation of water demand,and changes in the water quality sensors can create anomalouswater quality signatures, different from contaminationevents. Distinguishing normal behavior from abnormalbehavior is difficult in these situations. Second, due to thecomplex characteristics of water quality, a physical modeldescribing water quality behavior is difficult to derive andimplement in real time. This complexity has motivated theextensive use of data-driven models for predicting waterquality behavior. Although data-driven approaches haveproven to be an efficient alternative, several challenges persistincluding: accurate detection of contamination eventswith low false positive rates (FPRs) and low detection latency; definition of an adequate trade-off between sensitivityto contamination events and remaining insensitive tonon-event changes; characterization of faults due to waterquality sensor failures, numerical algorithmic instabilitiesand water process operation. In an attempt to alleviatesome of these challenges, research in both forecasting andmonitoring has been conducted.
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