There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Support vector classification (SVC) and regression (SVR) models were constructed and
applied to the surface water quality data to optimize the monitoring program. The
data set comprised of 1500 water samples representing 10 different sites monitored
for 15 years. The objectives of the study were to classify the sampling sites (spatial)
and months (temporal) to group the similar ones in terms of water quality with a view
to reduce their number; and to develop a suitable SVR model for predicting the biochemical
oxygen demand (BOD) of water using a set of variables. The spatial and temporal SVC
models rendered grouping of 10 monitoring sites and 12 sampling months into the clusters
of 3 each with misclassification rates of 12.39% and 17.61% in training, 17.70% and
26.38% in validation, and 14.86% and 31.41% in test sets, respectively. The SVR model
predicted water BOD values in training, validation, and test sets with reasonably
high correlation (0.952, 0.909, and 0.907) with the measured values, and low root
mean squared errors of 1.53, 1.44, and 1.32, respectively. The values of the performance
criteria parameters suggested for the adequacy of the constructed models and their
good predictive capabilities. The SVC model achieved a data reduction of 92.5% for
redesigning the future monitoring program and the SVR model provided a tool for the
prediction of the water BOD using set of a few measurable variables. The performance
of the nonlinear models (SVM, KDA, KPLS) was comparable and these performed relatively
better than the corresponding linear methods (DA, PLS) of classification and regression
modeling.