Daily Urban Water Demand Forecasting - Comparative Study

Conference paper
Part of the Communications in Computer and Information Science book series (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.

Keywords

Forecasting water demand Comparative study Averaging forecasts 

References

  1. 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. 2.
    Alvisi, S., Franchini, M., Marinelli, A.: A short-term, pattern-based model for water-demand forecasting. J. Hydroinform. 9(1), 39–50 (2007)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 4.
    Armstrong, J.S.: Principles of Forecasting: A Handbook for Researchers and Practitioners, vol. 30. Springer, Heidelberg (2001)Google Scholar
  5. 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)CrossRefGoogle Scholar
  6. 6.
    Berthold, M.R.: Mixed fuzzy rule formation. Int. J. Approx. Reason. 32(23), 67–84 (2003)CrossRefMATHGoogle Scholar
  7. 7.
    Brockwell, P., Davis, R.: Introduction to Time Series and Forecasting. Springer, New York (2002)CrossRefMATHGoogle Scholar
  8. 8.
    Caiado, J.: Performance of combined double seasonal univariate time series models for forecasting water demand. J. Hydrol. Eng. 15(3), 215–222 (2010)CrossRefGoogle Scholar
  9. 9.
    Clemen, R.T.: Combining forecasts: a review and annotated bibliography. Int. J. Forecast. 5(4), 559–583 (1989)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 11.
    Cowpertwait, P.S.P., Metcalfe, A.V.: Introductory Time Series with R, 1st edn. Springer Publishing Company, Incorporated, New York (2009)MATHGoogle Scholar
  12. 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)MathSciNetCrossRefMATHGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 14.
    Ellis, C., Wilson, P.: Another look at the forecast performance of ARFIMA models. Int. Rev. Financ. Anal. 13(1), 63–81 (2004)CrossRefGoogle Scholar
  15. 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. 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. 17.
    Gardner, E.S., Mckenzie, E.: Forecasting trends in time series. Manag. Sci. 31(10), 1237–1246 (1985)CrossRefMATHGoogle Scholar
  18. 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. 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)CrossRefGoogle Scholar
  20. 20.
    Johansen, S.: Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models. Econometrica 59(6), 1551–1580 (1991)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Jung, L., Box, G.: On a measure of lack of fit in time series models. Biometrika 65(2), 297–303 (1978)CrossRefMATHGoogle Scholar
  22. 22.
    KNIME: Professional open-source software. http://www.knime.org
  23. 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)CrossRefMATHGoogle Scholar
  24. 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)CrossRefGoogle Scholar
  25. 25.
    Machiwal, D., Jha, M.K.: Hydrologic Time Series Analysis: Theory and Practice. Springer, The Netherlands (2012)CrossRefGoogle Scholar
  26. 26.
    Magiera, E., Froelich, W.: Integrated support system for efficient water usage and resources management (ISS-EWATUS). Procedia Eng. 89, 1066–1072 (2014)CrossRefGoogle Scholar
  27. 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. 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)MathSciNetCrossRefMATHGoogle Scholar
  29. 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)CrossRefGoogle Scholar
  30. 30.
    Pearson, R.K.: Outliers in process modeling and identification. IEEE Trans. Control Syst. Technol. 10(1), 55–63 (2002)CrossRefGoogle Scholar
  31. 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)CrossRefGoogle Scholar
  32. 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)CrossRefGoogle Scholar
  33. 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)CrossRefGoogle Scholar
  34. 34.
    R: The R foundation for statistical computing. http://www.r-project.org
  35. 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)CrossRefGoogle Scholar
  36. 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. 37.
    Shmueli, G.: Practical Time Series Forecasting, 2nd edn. LLC, New York (2011). Statistics.comGoogle Scholar
  38. 38.
    Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Applications. Springer, New York (2000)CrossRefMATHGoogle Scholar
  39. 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)CrossRefGoogle Scholar
  40. 40.
    Timmermann, A., Codes, J.: Forecast combinations. In: Handbook of Economic Forecasting, pp. 135–196. Elsevier Press (2006)Google Scholar
  41. 41.
    Wallis, K.F.: Combining forecasts: forty years later. Appl. Financ. Econ. 21(1–2), 33–41 (2011)CrossRefGoogle Scholar
  42. 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)MathSciNetCrossRefMATHGoogle Scholar
  43. 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. 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)CrossRefGoogle Scholar
  45. 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 2011Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

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