| Summary: | The paper analyses the use of four data mining methods (Support Vector Machines. Cascade Neural Networks. Random Forests and Boosted Trees) to predict sorption on activated carbons. The input data for statistical models included the activated carbon parameters, organic substances and equilibrium concentrations in the solution. The assessment of the predictive abilities of the developed models was made with the use of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The computations proved that methods of data mining considered in the study can be applied to predict sorption of selected organic compounds 011 activated carbon. The lowest values of sorption prediction errors were obtained with the Cascade Neural Networks method (MAE = 1.23 g/g; MAPE = 7.90% and RMSE = 1.81 g/g), while the highest error values were produced by the Boosted Trees method (MAE=14.31 g/g; MAPE = 39.43% and RMSE = 27.76 g/g).
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