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Sewage Volume Forecasting on a Day-Ahead Basis – Analysis of Input Variables Uncertainty

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Water consumption and the resulting sewage volume (both strongly impacted by meteorological parameters) are of key importance for an efficient and sustainable operation of waterworks and wastewater treatment plants. Therefore, the objective of this research is to analyze the potential impact of input variables uncertainty on the performance of sewage volume forecasting model. The research is based on a real, three-year long, daily time series collected from Toruń (Poland). The used time series encompassed: sewage volume, water consumption, rainfall, temperature, precipitation, evaporation, sunshine duration and precipitation at a six hours interval. Neural network has been selected as a forecasting tool a multi-layer perceptron artificial. , a simulation model for the sewage volume was created which considered the above-mentioned time series as exogenous variables. Further, its performance was tested assuming that all non-historical input variables are prone to their individual forecasting errors. The analysis was dedicated firstly to each variable individually and later the potential of all of them being uncertain was tested. A lack of correlation between the input variables error was assumed. The research provides an interesting solution for visualizing the quality and actual performance of forecasting models where some or all of input variables has to be forecast.
Rocznik
Strony
70--79
Opis fizyczny
Bibliogr. 31 poz., rys., tab.
Twórcy
autor
  • AGH University, al. Mickiewicza 30, 30-059 Cracow, Poland
  • MDH University, Högskoleplan 1, 722 20 Västerås, Sweden
  • Nicolaus Copernicus University, Lwowska 1, 87-100 Toruń, Poland
  • Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland
Bibliografia
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  • 2. Bartkiewicz L., Szelag B., Studziński J. 2016. Impact Assessment of Input Variables and ANN Model Structure on Forecasting Wastewater Inflow into Sewage Treatment Plants (in Polish). Ochrona Środowiska, 38(2), 29–36.
  • 3. Beheshti A.M., Saegrov S., Ugarelli R. 2015. Infiltration/inflow assessment and detection in urban sewer system. Innsendte Artikler, 1, 24–34.
  • 4. Borowa A., Brdys M.A., Mazu K. 2007. Modelling of Wastewater Treatment Plant for Monitoring and Control Purposes by State – Space Wavelet Networks. International Journal of Computers, Commuication & Control, II(2), 121–131.
  • 5. Bowden G.J., Dandy G.C., Maier H.R. 2005. Input determination for neural network models in water resources applications. Part 1–background and methodology. Journal of Hydrology, 301(1–4), 75–92.
  • 6. Bugajski P.M., Kaczor G., Chmielowski K. 2017. Variable dynamics of sewage supply to wastewater treatment plant depending on the amount of precipitation water inflowing to sewerage network. Journal of Water and Land Development, 33(1), 57–63.
  • 7. Chmielewski K., Bugajski P., Kaczor G.B. 2016. Comparative analysis of the quality of sewage discharged from selected agglomeration sewerage systems. Journal of Water and Land Development, 30, 35–42.
  • 8. Cinar O. 2005. New tool for evaluation of performance of wastewater treatment plant: artificial neural network. Process Biochemistry, 40(9), 2980–2984.
  • 9. Czapczuk A., Dawidowicz J, Piekarski J. 2015. Artificial Intelligence Methods in the Design and Operation of Water Supply Systems. Rocznik Ochrona Srodowiska, 17, 1527–1544.
  • 10. Dellana S. A., West D. 2009. Predictive modeling for wastewater applications: Linear and nonlinear approaches. Environmental Modelling & Software, 24(1), 96–106.
  • 11. El-Din A.G., Smith D.W. 2002. Modelling approach for high flow rate in wastewater treatment operation. Journal of Environmental Engineering and Science, 1(4), 275–291.
  • 12. Elkhrachy I. 2015. Flash flood hazard mapping using satellite images and GIS tools: a case study of Najran City, Kingdom of Saudi Arabia (KSA). The Egyptian Journal of Remote Sensing and Space Science, 18(2), 261–278.
  • 13. Fernandez F.J., Seco A., Ferrer J., Rodrigo M.A. 2009. Use of neurofuzzy networks to improve wastewater flow-rate forecasting. Environmental Modelling and Software, 24, 686–693.
  • 14. 14. Jia H., Zhang T., Yin X., Shang M., Chen F., Lei Y., Chu Q. 2019. Impact of Climate Change on the Water Requirements of Oat in Northeast and North China. Water, 11(1), 91.
  • 15. Kaczor G., Chmielowski K., Bugajski P. 2017. The Effect of Total Annual Precipitation on the Volume of Accidental Water Entering Sanitary Sewage System. Annual Set The Environment Protection, 19, 668–681.
  • 16. Kavzoglu T. 1999. Determining optimum structure for artificial neural networks. Proc. Remote Sensing Society, 675–682.
  • 17. Kaźmierczak B. 2013. Mathematical Modeling of Storm Overflow with a Cylindrical Vortex Regulator. Annual Set The Environment Protection, 2158–2174.
  • 18. Kaźmierczak B., Kotowski A. 2014. The influence of precipitation intensity growth on the urban drainage systems designing. Theoretical and Applied Climatology, 118(1–2), 285–296.
  • 19. Kutyłowska M. 2017. Prediction of failure frequency of water-pipe network in the selected city. Periodica Polytechnica Civil Engineering, 61(3), 548–553.
  • 20. Kutyłowska M., Hotloś H. 2012. History, structure and deterioration of sewerage system in Wrocław. Environment Protection Engineering, 38(3), 145–157.
  • 21. Ma M., He B., Wan J., Jia P., Guo X., Gao L., Maguire L.W, Hong Y. 2018. Characterizing the Flash Flooding Risks from 2011 to 2016 over China. Water, 10(6), 704.
  • 22. Marszelewski W., Piasecki A. 2017. Effect of television broadcasts of global sporting events on shortterm changes in the use of water from the water supply network. Journal of Water Sanitation and Hygiene for Development, 7(4), 623–629.
  • 23. Młyński D., Kurek K., Bugajski P. 2018. An Analysis of Seasonal Waste Draining for the Urban Agglomeration Using Statistical Methods. Water, 10(8), 976.
  • 24. Obarska-Pempkowiak H., Kołecka K., Gajewska M., Wojciechowska E., Ostojski A. 2015. Sustainable sewage management in rural areas. Annual Set The Environment Protection, 17, 585–602.
  • 25. Pawęska K., Duda P. 2018. Impact of precipitation on the balance of wastewater treated in municipal wastewater treatment plant. Ecological Engineering, 19(6), 49–56.
  • 26. Shibata K., Ikeda Y. 2009. Effect of number of hidden neurons on learning in large-scale layered neural networks. Proc. ICCAS-SICE, 5008–5013.
  • 27. Szeląg B., Bartkiewicz L., Studziński J. 2016. Blackbox forecasting of selected indicator values for influent wastewater quality in municipal treatment plant (in Polish). Ochrona Środowiska, 38(4), 39–46.
  • 28. Szeląg B., Studziński J., Chmielowski K., Leśniańska A., Rojek I. 2018. Forecasting the sewage inflow into a treatment plant using artificial neural networks and linear discriminant analysis (in Polish). Ochrona Środowiska, 40(4).
  • 29. Wallace J.M., Held I.M., Thompson D.W., Trenberth K.E., Walsh J.E. 2014. Global warming and winter weather. Science, 343(6172), 729–730.
  • 30. Yap H.T., Ngien S.K. 2017. Assessment on inflow and infiltration in sewerage systems of Kuantan, Pahang. Water Science and Technology, 76, 2918–2927.
  • 31. Yin J., Yu D., Yin Z., Liu M., He, Q. 2016. Evaluating the impact and risk of pluvial flash flood on intra-urban road network: A case study in the city center of Shanghai, China. Journal of hydrology, 537, 138–145.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7276bac5-77b9-4ff3-bc92-8a4c3a0daa1f
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