@ARTICLE{Szeląg_Bartosz_Evaluation_2017,
 author={Szeląg, Bartosz and Bartkiewicz, Lidia and Studziński, Jan and Barbusiński, Krzysztof},
 volume={vol. 43},
 number={No 3},
 journal={Archives of Environmental Protection},
 howpublished={online},
 year={2017},
 publisher={Polish Academy of Sciences},
 abstract={The aim of the study was to evaluate the possibility of applying different methods of data mining to model the inflow of sewage into the municipal sewage treatment plant. Prediction models were elaborated using methods of support vector machines (SVM), random forests (RF), k-nearest neighbour (k-NN) and of Kernel regression (K). Data consisted of the time series of daily rainfalls, water level measurements in the clarified sewage recipient and the wastewater inflow into the Rzeszow city plant. Results indicate that the best models with one input delayed by 1 day were obtained using the k-NN method while the worst with the K method. For the models with two input variables and one explanatory one the smallest errors were obtained if model inputs were sewage inflow and rainfall data delayed by 1 day and the best fit is provided using RF method while the worst with the K method. In the case of models with three inputs and two explanatory variables, the best results were reported for the SVM and the worst for the K method. In the most of the modelling runs the smallest prediction errors are obtained using the SVM method and the biggest ones with the K method. In the case of the simplest model with one input delayed by 1 day the best results are provided using k-NN method and by the models with two inputs in two modelling runs the RF method appeared as the best.},
 type={Artykuły / Articles},
 title={Evaluation of the impact of explanatory variables on the accuracy of prediction of daily inflow to the sewage treatment plant by selected models nonlinear},
 URL={http://ochroma.man.poznan.pl/Content/102535/PDF/aep-2017-0030.pdf},
 doi={10.1515/aep-2017-0030},
 keywords={wastewater treatment plant, Data Mining, random forest, forecasting inflow, k – nearest neighbour, Kernel regression},
}