Details

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

Journal title

Archives of Environmental Protection

Yearbook

2017

Numer

No 3

Publication authors

Divisions of PAS

Nauki Techniczne

Description

Archives of Environmental Protection is the oldest Polish scientific journal of international scope that publishes articles on engineering and environmental protection. The quarterly has been published by the Institute of Environmental Engineering, Polish Academy of Sciences since 1975. The journal has served as a forum for the exchange of views and ideas among scientists. It has become part of scientific life in Poland and abroad. The quarterly publishes the results of research and scientific inquiries by best specialists hereby becoming an important pillar of science. The journal facilitates better understanding of environmental risks to humans and ecosystems and it also shows the methods for their analysis as well as trends in the search of effective solutions to minimize these risks. The journal is indexed by Thomson Reuters services (Biological Abstract, BIOSIS Previews) and has an Impact Factor 2017 of 1.120

Publisher

Polish Academy of Sciences

Date

2017

Identifier

ISSN 2083-4772 ; eISSN 2083-4810

References

Breiman (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.

DOI

10.1515/aep-2017-0030

×