TitleEvaluation of the impact of explanatory variables on the accuracy of prediction of daily inflow to the sewage treatment plant by selected models nonlinear
Journal titleArchives of Environmental Protection
Keywordswastewater treatment plant ; Data Mining ; random forest ; forecasting inflow ; k – nearest neighbour ; Kernel regression
Divisions of PASNauki Techniczne
PublisherPolish Academy of Sciences
TypeArtykuły / Articles
IdentifierISSN 2083-4772 ; eISSN 2083-4810
ReferencesBreiman (2000), Random forests, Journal Machine Learning, 45, 5. ; IMGW (2008), The daily time series of precipitation of the Airport Meteorological Station Rzeszów from the period, null, 2005. ; Nesmerak (2014), Analysis of the time series of waste water quality at the inflow of the wastewater treatment plant and transfer functions of and, Journal Hydrology Hydromechanics, 1. ; Piotrowski (2006), Flash - flood forecasting by means of neural networks and nearest neighbour approach a comparative study Nonlinear Processes, Geophysics, 13, 443. ; Wei (null), Short - term prediction of influent flow in wastewater treatment plant, Stochastic Environmental Research and Risk Assessment, 29, 2015. ; Szeląg (2016), Application of selected methods of artificial intelligence to activated sludge settleability predictions, Polish Journal of Environmental Studies, 25, 1709. ; Piotrowski (2014), Comparing large number of metaheurestics for artificial neural networks training to predict water temperature in a natural river Computers, Geosciences, 136. ; Abyaneh (2014), Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters, Journal of Environmental Health Science Engineering, 12, 1. ; Bartkiewicz (2010), modeling of the hydraulic load of communal wastewater networks in Modeling eds, Mathematical Simulation, 156. ; Henze (2000), Activated Sludge Models Publishing, null. ; Simonoff (1996), Smoothing in Springer in New York, Methods Statistics Series Statistics. ; Vapnik (1998), Statistical Learning Theory New York, null. ; Jonsdottir (2007), Conditional parametric models for storm sewer runoff, Water Resources Research, 43, 1. ; Adamowski (2012), Comparison of multivariate adaptive regression splines with copuled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro watersheds with limited data of, Journal Hydroinformatics, 14, 731. ; Friedman (2001), Greedy function approximation gradient boosting machine The of, Annals Statistics, 29, 1189. ; Rutkowski (2006), Techniques in Polish, Computational Intelligence Methods. ; Abhart (2002), See Multi - model data fusion for river flow forecasting : an evaluation of six alternative methods based on two contrasting catchments and System, Hydrology Earth Sciences, 6, 655. ; Chuchro (2009), Prediction of the sewage treatement plant inflow parameters i in Polish, null. ; Dellana (2009), Predictive modeling for wastewater applications : Linear and nonlinear approaches Environmental Modelling and Software, null, 24, 96. ; Banasik (2014), Curve number estimation for a small urban catchment from recorded rainfall runoff events of Environmental Protection, Archives, 40, 75. ; Friedman (2002), Stochastic gradient boosting Data, Computational Statistics Analysis, 38, 367. ; Box (1976), Time series analysis Forecasting control San, null. ; Han (2016), computing method to predict sludge volume index based on a recurrent self - organizing neural network, soft Applied Soft Computing, 477. ; Borowa (2007), Modeling of wastewater treatment plant for monitoring and control purposes by state - space wavelet networks of, International Journal Computers Communications Control, 121. ; Anderson (2001), Data - based mechanistic modeling and validation of rainfall - flow processes in Model validation : perspectives in hydrological eds, Young science. ; Smith (2002), Din Modelling approach for high flow rate in wastewater treatment operation of and, Journal Environmental Engineering Science, 1. ; Koza (1992), On the Programming of Computers by Natural Selection MIT, Genetic Programming. ; Kulczycki (2005), Nuclear estimators in system analysis, null. ; Bartkiewicz (2016), Impact assessment of input variables and ANN model structure on forecasting wastewater inflow into sewage treatment plants in Polish, null, 38, 29. ; Fernandez (2009), Use of neurofuzzy networks to improve wastewater flow - rate forecasting Environmental Modelling and Software, null, 24, 686. ; Licznar (2004), Rainfall erosivity prediction in Poland on the basis of monthly precipitation totals of Environmental Protection in Polish, Archives, 30, 29.