Support vector machines in water quality management

The SVM model is tested in the current study to predict the surface water BOD using set of selected directly measurable parameters. This figure shows a good correlation between the predicted and measured BOD values. [Display omitted] ► SVM classification and regression models applied to a large surface water quality data set to optimize monitoring program. ► SVM classification (spatial and temporal) model grouping similar sampling sites and months rendered 92.5% data reduction. ► SVM regression model using a set of six directly measurable variables predicted water BOD precisely ( R > 0.90). ► Performances of SVM, KDA, KPLS comparable, and relatively better than DA and PLS. ► SVM based modeling has helped in redesigning the water quality monitoring program. 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.