Forecasting Daily Water Demand Using Fuzzy Cognitive Maps

  • Jose L. Salmeron
  • Wojciech Froelich
  • Elpiniki I. Papageorgiou
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)


In this chapter, we describe the design of a multi-regressive forecasting model based on fuzzy cognitive maps (FCMs). Growing window approach and 1-day ahead forecasting are assumed. The proposed model is retrained every day as more data become available. To improve forecasting accuracy, mean daily temperature and precipitation are applied as additional explanatory variables. The designed model is trained and tested using data gathered from a water distribution system. Comparative experiments provide evidence for the superiority of the proposed approach over the selected state-of-the-art competitive methods.


Forecasting water demand Fuzzy cognitive maps 



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. The authors would like to thank the water distribution company in Sosnowiec (Poland) for gathering water demand data and the personal of the weather station of the University of Silesia for collecting and preparing meteorological data.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jose L. Salmeron
    • 1
  • Wojciech Froelich
    • 2
  • Elpiniki I. Papageorgiou
    • 3
  1. 1.University Pablo de OlavideSevilleSpain
  2. 2.The University of SilesiaSosnowiecPoland
  3. 3.Computer Engineering DepartmentTechnological Educational Institute of Central GreeceLamiaGreece

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