Data validation and missing data reconstruction using self-organizing map for water treatment

Applications in the water treatment domain
generally rely on complex sensors located at remote sites.
The processing of the corresponding measurements for
generating higher-level information such as optimization of
coagulation dosing must therefore account for possible
sensor failures and imperfect input data. In this paper, selforganizing
map (SOM)-based methods are applied to
multiparameter data validation and missing data reconstruction
in a drinking water treatment. The SOM is a
special kind of artificial neural networks that can be used
for analysis and visualization of large high-dimensional
data sets. It performs both in a nonlinear mapping from a
high-dimensional data space to a low-dimensional space
aiming to preserve the most important topological and
metric relationships of the original data elements and, thus,
inherently clusters the data. Combining the SOM results
with those obtained by a fuzzy technique that uses marginal
adequacy concept to identify the functional states (normal
or abnormal), the SOM performances of validation and
reconstruction process are tested successfully on the
experimental data stemming from a coagulation process
involved in drinking water treatment.