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Prediction of effluent arsenic concentration of wastewater treatment plants using machine learning and kriging-based models
Citation key Zounemat-Kermani2021-springer
Author Zounemat-Kermani, Mohammad and Alizamir, Meysam and Keshtegar, Behrooz and Batelaan, Okke and Hinkelmann, Reinhard
Year 2021
Journal Environ. Sci. Pollut. Res. Int.
Month nov
Publisher Springer Science and Business Media LLC
Abstract This study evaluates 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, the forward selection k-fold cross-validation method was employed 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 not only made the architecture of the model simpler and more effective, but also enhanced the performance of the developed models (e.g., around 7.8\% performance enhancement of the RMSE). Although the standard kriging method provided the least good predictive results (RMSE = 0.18 ug/l and NSE=0.75), it was revealed that the kriging-logistic method gave the best performance among the applied models (RMSE = 0.11 ug/l and NSE=0.90).
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