Prediction of Effluent Arsenic Concentration of Wastewater Treatment Plants Using Machine Learning and Kriging-Based Models

We evaluate the potential of kriging-based (kriging and kriging-logistic) and machine learning models (MARS, GBRT, and ANN) in predicting the effluent arsenic concentration of a wastewater treatment plant. Two distinct input combination scenarios were established, using seven quantitative and qualitative independent influent variables. In the first scenario, all of the seven independent variables were taken into account for constructing the data-driven models. For the second input scenario, we used the forward selection k-fold cross-validation method to select effective explanatory influent parameters. The results obtained from both input scenarios show that the kriging-logistic and machine learning models are effective and robust. However, using the feature selection procedure in the second scenario, made not only the architecture of the model simpler and more effective, but also enhanced the performance of the developed models. Although the standard kriging method failed to provide fair predictive results, it was revealed that the kriging-logistic method gave the best performance among the applied models.