Skip to main content
SpringerLink
Log in

Daily Urban Water Demand Forecasting - Comparative Study

  • Conference paper
  • First Online:
Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery (BDAS 2015, BDAS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 613))

Abstract

There are many existing, general purpose models for the forecasting of time series. However, until now, only a small number of experimental studies exist whose goal is to select the forecasting model for a daily, urban water demand series. Moreover, most of the existing studies assume off-line access to data. In this study, we are confronted with the task to select the best forecasting model for the given water demand time series gathered from the water distribution system of Sosnowiec, Poland. In comparison to the existing works, we assume on-line availability of water demand data. Such assumption enables day-by-day retraining of the predictive model. To select the best individual approach, a systematic comparison of numerous state-of-the-art predictive models is presented. For the first time in this paper, we evaluate the approach of averaging forecasts with respect to the on-line available daily water demand time series. In addition, we analyze the influence of missing data, outliers, and external variables on the accuracy of forecasting. The results of experiments provide evidence that the average forecasts outperform all considered individual models, however, the selection of the models used for averaging is not trivial and must be carefully done. The source code of the preformed experiments is available upon request.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adamowski, J., Fung Chan, H., Prasher, S.O., Ozga-Zielinski, B., Sliusarieva, A.: Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in montreal, canada. Water Resour. Res. 48(1) (2012)

    Google Scholar 

  2. Alvisi, S., Franchini, M., Marinelli, A.: A short-term, pattern-based model for water-demand forecasting. J. Hydroinform. 9(1), 39–50 (2007)

    Article  Google Scholar 

  3. An, A., Chan, C.W., Shan, N., Cercone, N., Ziarko, W.: Applying knowledge discovery to predict water-supply consumption. IEEE Expert 12(4), 72–78 (1997)

    Article  Google Scholar 

  4. Armstrong, J.S.: Principles of Forecasting: A Handbook for Researchers and Practitioners, vol. 30. Springer, Heidelberg (2001)

    Google Scholar 

  5. Bardossy, A.: Fuzzy rule-based flood forecasting. In: Abrahart, R.J., See, L.M., Solomatine, D.P. (eds.) Practical Hydroinformatics. Water Science and Technology Library, vol. 68, pp. 177–187. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  6. Berthold, M.R.: Mixed fuzzy rule formation. Int. J. Approx. Reason. 32(23), 67–84 (2003)

    Article  MATH  Google Scholar 

  7. Brockwell, P., Davis, R.: Introduction to Time Series and Forecasting. Springer, New York (2002)

    Book  MATH  Google Scholar 

  8. Caiado, J.: Performance of combined double seasonal univariate time series models for forecasting water demand. J. Hydrol. Eng. 15(3), 215–222 (2010)

    Article  Google Scholar 

  9. Clemen, R.T.: Combining forecasts: a review and annotated bibliography. Int. J. Forecast. 5(4), 559–583 (1989)

    Article  Google Scholar 

  10. Cortez, P.C., Rocha, M., Neves, J.: Genetic and evolutionary algorithms for time series forecasting. In: Monostori, L., Váncza, J., Ali, M. (eds.) IEA/AIE 2001. LNCS (LNAI), vol. 2070, pp. 393–402. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  11. Cowpertwait, P.S.P., Metcalfe, A.V.: Introductory Time Series with R, 1st edn. Springer Publishing Company, Incorporated, New York (2009)

    MATH  Google Scholar 

  12. Dickey, D.A., Fuller, W.A.: Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74(366), 427–431 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  13. Donkor, E., Mazzuchi, T., Soyer, R., Alan Roberson, J.: Urban water demand forecasting: review of methods and models. J. Water Resour. Plan. Manag. 140(2), 146–159 (2014)

    Article  Google Scholar 

  14. Ellis, C., Wilson, P.: Another look at the forecast performance of ARFIMA models. Int. Rev. Financ. Anal. 13(1), 63–81 (2004)

    Article  Google Scholar 

  15. Froelich, W.: Dealing with seasonality while forecasting urban water demand. In: Neves-Silva, R., Jain, L.C., Howlett, R.J. (eds.) Intelligent Decision Technologies, pp. 171–180. Springer International Publishing, Switzerland (2015)

    Google Scholar 

  16. Froelich, W.: Forecasting daily urban water demand using dynamic gaussian Bayesian network. In: Kozielski, S., Mrozek, D., Kasprowski, P., Malysiak-Mrozek, B., Kostrzewa, D. (eds.) Beyond Databases, Architectures and Structures, pp. 333–342. Springer International Publishing, Switzerland (2015)

    Google Scholar 

  17. Gardner, E.S., Mckenzie, E.: Forecasting trends in time series. Manag. Sci. 31(10), 1237–1246 (1985)

    Article  MATH  Google Scholar 

  18. Hyndman, R.J.: forecast: Forecasting functions for time series and linear models (2015). http://github.com/robjhyndman/forecast (r package version 6.2)

  19. Jach, T., Magiera, E., Froelich, W.: Application of HADOOP to store and process big data gathered from an urban water distribution system. Procedia Eng. 119, 1375–1380 (2015)

    Article  Google Scholar 

  20. Johansen, S.: Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models. Econometrica 59(6), 1551–1580 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  21. Jung, L., Box, G.: On a measure of lack of fit in time series models. Biometrika 65(2), 297–303 (1978)

    Article  MATH  Google Scholar 

  22. KNIME: Professional open-source software. http://www.knime.org

  23. Kwiatkowski, D., Phillips, P.C., Schmidt, P., Shin, Y.: Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? J. Econom. 54(13), 159–178 (1992)

    Article  MATH  Google Scholar 

  24. Liu, J.Q., Zhang, T.Q., Yu, S.K.: Chaotic phenomenon and the maximum predictable time scale of observation series of urban hourly water consumption. J. Zhejiang Univ. Sci. 5(9), 1053–1059 (2004)

    Article  Google Scholar 

  25. Machiwal, D., Jha, M.K.: Hydrologic Time Series Analysis: Theory and Practice. Springer, The Netherlands (2012)

    Book  Google Scholar 

  26. Magiera, E., Froelich, W.: Integrated support system for efficient water usage and resources management (ISS-EWATUS). Procedia Eng. 89, 1066–1072 (2014)

    Article  Google Scholar 

  27. Magiera, E., Froelich, W.: Application of Bayesian networks to the forecasting of daily water demand. In: Neves-Silva, R., Jain, L., Howlett, R. (eds.) Intelligent Decision Technologies, pp. 385–393. Springer International Publishing, Switzerland (2015)

    Google Scholar 

  28. MatíAs, J.M., Febrero-Bande, M., GonzáLez-Manteiga, W., Reboredo, J.C.: Boosting garch and neural networks for the prediction of heteroskedastic time series. Math. Comput. Model. 51(3–4), 256–271 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  29. Mavromatidis, L.E., Bykalyuk, A., Lequay, H.: Development of polynomial regression models for composite dynamic envelopes thermal performance forecasting. Appl. Energy 104, 379–391 (2013)

    Article  Google Scholar 

  30. Pearson, R.K.: Outliers in process modeling and identification. IEEE Trans. Control Syst. Technol. 10(1), 55–63 (2002)

    Article  Google Scholar 

  31. Pulido-Calvo, I., Gutirrez-Estrada, J.C.: Improvedfrigation water demand forecasting using a soft-computing hybrid model. Biosyst. Eng. 102(2), 202–218 (2009)

    Article  Google Scholar 

  32. Pulido-Calvo, I., Montesinos, P., Roldn, J., Ruiz-Navarro, F.: Linear regressions and neural approaches to water demand forecasting in irrigation districts with telemetry systems. Biosyst. Eng. 97(2), 283–293 (2007)

    Article  Google Scholar 

  33. Qi, C., Chang, N.B.: System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts. J. Environ. Manag. 92(6), 1628–1641 (2011)

    Article  Google Scholar 

  34. R: The R foundation for statistical computing. http://www.r-project.org

  35. Reeves, G.R., Lawrence, K.D., Lawrence, S.M., Guerard Jr., J.B.: Combining earnings forecasts using multiple objective linear programming. Comput. Oper. Res. 15(6), 551–559 (1988)

    Article  Google Scholar 

  36. Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN), pp. 586–591 (1993)

    Google Scholar 

  37. Shmueli, G.: Practical Time Series Forecasting, 2nd edn. LLC, New York (2011). Statistics.com

    Google Scholar 

  38. Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Applications. Springer, New York (2000)

    Book  MATH  Google Scholar 

  39. Tersvirta, T., Lin, C.F., Granger, C.W.J.: Power of the neural network linearity test. J. Time Ser. Anal. 14(2), 209–220 (1993)

    Article  Google Scholar 

  40. Timmermann, A., Codes, J.: Forecast combinations. In: Handbook of Economic Forecasting, pp. 135–196. Elsevier Press (2006)

    Google Scholar 

  41. Wallis, K.F.: Combining forecasts: forty years later. Appl. Financ. Econ. 21(1–2), 33–41 (2011)

    Article  Google Scholar 

  42. Xiao, Z., Gong, K., Zou, Y.: A combined forecasting approach based on fuzzy soft sets. J. Comput. Appl. Math. 228(1), 326–333 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  43. Xizhu, W.: Forecasting of urban water demand based on Chaos theory. In: Control Conference, CCC 2007, pp. 441–444, July 2007. (Chinese)

    Google Scholar 

  44. Yasar, A., Bilgili, M., Simsek, E.: Water demand forecasting based on stepwise multiple nonlinear regression analysis. Arab. J. Sci. Eng. 37(8), 2333–2341 (2012)

    Article  Google Scholar 

  45. Yin-shan, X., Ya-dong, M., Ting, Y.: Combined forecasting model of urban water demand under changing environment. In: 2011 International Conference on Electric Technology and Civil Engineering (ICETCE), pp. 1103–1107, April 2011

    Google Scholar 

Download references

Acknowledgments

The work was supported by ISS-EWATUS project which has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement No. 619228.

Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Silesia, Sosnowiec, Poland

    Wojciech Froelich

Authors
  1. Wojciech Froelich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wojciech Froelich .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Froelich, W. (2016). Daily Urban Water Demand Forecasting - Comparative Study. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS BDAS 2015 2016. Communications in Computer and Information Science, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-34099-9_49

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-34099-9_49

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34098-2

  • Online ISBN: 978-3-319-34099-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation