Soft Computing Approaches for Urban Water Demand Forecasting

  • Konstantinos Kokkinos
  • Elpiniki I. Papageorgiou
  • Katarzyna Poczeta
  • Lefteris Papadopoulos
  • Chrysi Laspidou
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 57)

Abstract

This paper presents an integrated framework for water resources management at urban level which consists of a Neuro-Fuzzy and Fuzzy Cognitive Map-based, (FCM) decision support system (DSS) based on multiple objectives and multiple disciplines for planning and forecasting. The proposed DSS has as primary goals to: (a) adaptively control the water pressure of the water distribution system by forecasting the water demand at the urban level and (b) to reduce leakage of the water network by controlling the water pressure. The system follows a model-driven architecture with the inclusion of the FCM-based models and a spatio-temporal model for arranging all data. The validation of the proposed learning algorithms is made for two case studies that comprise different water supply characteristics and correspond to different locations in Europe.

Keywords

Fuzzy Cognitive Maps Neuro-Fuzzy Water management Forecasting Prediction Decision support 

References

  1. 1.
    Serrat-Capdevila, A., Valdes, J.B., Gupta, H.V.: Decision support systems in water resources planning and management: stakeholder participation and the sustainable path to science-based decision making. In: Jao, C. (ed.) Efficient Decision Support Systems—Practice and Challenges from Current to Future, pp. 423–440. INTECH (2011)Google Scholar
  2. 2.
    Ocampo-Martinez, C., Puig, V., Cembrano, G., Quevedo, J.: Application of predictive control strategies to the management of complex networks in the urban water cycle. IEEE Control Syst. Mag. 33, 15–45 (2013)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bakker, M., Vreeburg, J.H.G., van Schagen, J.H.G., Rietveld, L.C.: A fully adaptive forecasting model for short-term drinking water demand. Environ. Model. Softw. 48, 141–151 (2013). ISSN: 1364-8152Google Scholar
  4. 4.
    Makropoulos, C.K., Natsis, K., Liu, S., Mittas, K., Butler, D.: Decision support for sustainable option selection in integrated urban water management. Environ. Model. Softw. 23(12), 1448-1460, (2008). ISSN: 1364-8152Google Scholar
  5. 5.
    Pouget, L., Escaler, I., Guiu, R., Mc Ennis, S., Versini, P.: Global change adaptation in water resources management: the water change project. Sci. Total Environ. 186–193 (2012). ISSN: 0048-9697 440Google Scholar
  6. 6.
    Laucelli, D., Berardi, L., Giustolisi, O.: Assessing climate change and asset deterioration impacts on water distribution networks: demand-driven or pressure-driven network modeling? Environ. Model. Softw. 206–216 (2012). ISSN: 1364-815237Google Scholar
  7. 7.
    Savić, D.A., Bicik, J., Morley, M.S.: A DSS generator for multi objective optimization of spreadsheet-based models. Environ. Model. Softw. 26, 551–561 (2011)Google Scholar
  8. 8.
    Wu, Z.Y., Sage, P.: Water loss detection via genetic algorithm optimization based model calibration. In: 8th Annual International Symposium on Water Distribution System Analysis, 27–30 Aug, Cincinnati, Ohio (2006)Google Scholar
  9. 9.
    Quevedo, J., Cugueró, M., Pérez, R., Nejjari, F., Puig, V., Mirats, J.: Leakage location in water distribution networks based on correlation measurement of pressure sensors. In: IWA Symposium on Systems Analysis and Integrated Assessment. San Sebastian (2011)Google Scholar
  10. 10.
    Tabesh, M., Dini, M.: Fuzzy and neuro-fuzzy models for short-term water demand forecasting in Tehran. Iran. J. Sci. Technol. Trans. B Eng. 33(B1), 61–77 (2009)Google Scholar
  11. 11.
    Yurdusev, M.A., Firat, M.: Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: an application to Izmir, Turkey. J. Hydrol. 365, 225–234 (2009)CrossRefGoogle Scholar
  12. 12.
    Firat, M., Turkan, M.E., Yurdusev, M.A.: Comparative analysis of fuzzy inference systems for water consumption time series prediction. J. Hydrol. 374, 235–241 (2009)CrossRefGoogle Scholar
  13. 13.
    Mellios, N., Kofinas, D., Papageorgiou, E., Laspidou, C.: A multivariate analysis of the daily water demand of Skiathos Island, Greece. In: Implementing the Artificial Neuro-Fuzzy Inference Sysytem (ANFIS), IAHR 2015, E-Proceedings of the 36th International Association for Hydro-Environment Engineering and Research World Congress, 28 June–3 July. Hague, Netherlands (2015)Google Scholar
  14. 14.
    Song, H., Miao, C., Roel, W., Shen, Z.: Implementation of fuzzy cognitive maps based on fuzzy neural network and application in prediction of time series. IEEE Trans. Fuzzy Syst. 18(2), 233–250 (2010)Google Scholar
  15. 15.
    Homenda, W., Jastrzebska, A., and Pedrycz, W.: Modeling time series with fuzzy cognitive maps. In: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, China, pp. 2055–2062 (2014)Google Scholar
  16. 16.
    Homenda, W., Jastrzebska, A., Pedrycz, W.: Nodes selection criteria for fuzzy cognitive maps designed to model time series. Adv. Intell. Syst. Comput. 323, 859–870 (2015)CrossRefGoogle Scholar
  17. 17.
    Salmeron, J.L., Froelich, W., Papageorgiou, E.I.: Application of fuzzy cognitive maps to the forecasting of daily water demand. Presented at ITISE 2015 (International Work-Conference on Time Series), 1–3 July, Granada, Spain (2015)Google Scholar
  18. 18.
    Papageorgiou, E., Froelich, W.: Multi-step prediction of pulmonary infection with the use of evolutionary fuzzy cognitive maps. Neurocomputing 92, 28–35 (2012)CrossRefGoogle Scholar
  19. 19.
    Papageorgiou, E., Poczeta, K., and Laspidou, C.: Application of fuzzy cognitive maps to water demand prediction. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZIEEE), Istanbul, pp. 1–8 (2015)Google Scholar
  20. 20.
    Kosko, B.: Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Prentice Hall, New York (1992)MATHGoogle Scholar
  21. 21.
    Papageorgiou, E.I.: Fuzzy Cognitive Maps for Applied Sciences and Engineering from Fundamentals to Extensions and Learning Algorithms. Springer (2014)Google Scholar
  22. 22.
    Lu, W., Pedrycz, W., Liu, X., Yang, J., Li, P.: The modeling of time series based on fuzzy information granules. Exp. Syst. Appl. 41, 3799–3808 (2014)CrossRefGoogle Scholar
  23. 23.
    Lu, W., Yang, J., Liu, X.: The hybrids algorithm based on fuzzy cognitive map for fuzzy time series prediction. J. Inf. Comput. Sci. 11(2), 357–366 (2014)CrossRefGoogle Scholar
  24. 24.
    Girard, M., Stewart, R.A.: Implementation of pressure and leakage management strategies on the gold coast, Australia: case study. J. Water Resour. Plann. Manage. 133, 210–217 (2007)CrossRefGoogle Scholar
  25. 25.
    Anzaldi, G., Rubion, E., Corchero, A., Sanfeliu, R., Domingo, X., Pijuan, J., Tersa, F.: Towards an enhanced knowledge-based Decision Support System (DSS) for Integrated Water Resource Management (IWRM). Procedia Eng. 89, 1097–1104 (2014)CrossRefGoogle Scholar
  26. 26.
    Billings, B.R., Jones, V.C.: Forecasting Urban Water Demand, vol. 7. American Water Works Associations (2008)Google Scholar
  27. 27.
    Kofinas, D., Mellios, N., Papageorgiou, E., Laspidou, C.: Urban water demand forecasting for the island of Skiathos. Procedia Eng. 89, 1023–1030 (2014)CrossRefGoogle Scholar
  28. 28.
    Laspidou, C.S., Kofinas, D., Mellios, N., Papageorgiou, E., Froelich, W., Magiera, E.: Urban water demand forecasting for the Island of Skiathos using multivariate analysis. In: IWA Water IDEAS Conference Proceedings, Bologna, 22–24 Oct, Italy (2014)Google Scholar
  29. 29.
    Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst. 153(3), 371–401 (2005)MathSciNetCrossRefMATHGoogle Scholar
  30. 30.
    Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing, p. 607. Prentice Hall, Englewood Cliffs (1997). ISBN: 0-13-261066-3Google Scholar
  31. 31.
    Pulido-Calvo, I., Gutierrez-Estrada, J.C.: Improved irrigation water demand forecasting using a soft-computing hybrid model. Biosyst. Eng. 102(2), 202–218 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Konstantinos Kokkinos
    • 1
  • Elpiniki I. Papageorgiou
    • 2
    • 3
  • Katarzyna Poczeta
    • 4
  • Lefteris Papadopoulos
    • 1
  • Chrysi Laspidou
    • 5
  1. 1.Information Technologies InstituteThermiGreece
  2. 2.Department of Computer EngineeringTechnological Education Institute/University of Applied Sciences of Central GreeceLamiaGreece
  3. 3.Faculty of Business EconomicsHasselt UniversityHasseltBelgium
  4. 4.Department of Information SystemsKielce University of TechnologyKielcePoland
  5. 5.Department of Civil EngineeringUniversity of ThessalyNea IoniaGreece

Personalised recommendations