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Intelligent Decision Technologies 2016 pp 357–367Cite as

Soft Computing Approaches for Urban Water Demand Forecasting

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Part of the book series: Smart Innovation, Systems and Technologies ((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.

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Author information

Authors and Affiliations

  1. Information Technologies Institute, CERTH, 6th km Charilaou-Thermi Rd., 57001, Thermi, Greece

    Konstantinos Kokkinos & Lefteris Papadopoulos

  2. Department of Computer Engineering, Technological Education Institute/University of Applied Sciences of Central Greece, Lamia, Greece

    Elpiniki I. Papageorgiou

  3. Faculty of Business Economics, Hasselt University, Hasselt, Belgium

    Elpiniki I. Papageorgiou

  4. Department of Information Systems, Kielce University of Technology, al. Tysiąclecia Państwa Polskiego 7, 25-314, Kielce, Poland

    Katarzyna Poczeta

  5. Department of Civil Engineering, University of Thessaly, Nea Ionia, Greece

    Chrysi Laspidou

Authors
  1. Konstantinos Kokkinos
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  2. Elpiniki I. Papageorgiou
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  3. Katarzyna Poczeta
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  4. Lefteris Papadopoulos
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  5. Chrysi Laspidou
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Corresponding author

Correspondence to Elpiniki I. Papageorgiou .

Editor information

Editors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Artificial Intelligence Department, Universidad Politécnica de Madrid, Madrid, Spain

    Alfonso Mateos Caballero

  3. KES International, Shoreham-by-sea, UK

    Robert J. Howlett

  4. University of Canberra, Canberra, Australia

    Lakhmi C. Jain

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© 2016 Springer International Publishing Switzerland

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Kokkinos, K., Papageorgiou, E.I., Poczeta, K., Papadopoulos, L., Laspidou, C. (2016). Soft Computing Approaches for Urban Water Demand Forecasting. In: Czarnowski, I., Caballero, A.M., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies 2016. Smart Innovation, Systems and Technologies, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-39627-9_31

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  • DOI: https://doi.org/10.1007/978-3-319-39627-9_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39626-2

  • Online ISBN: 978-3-319-39627-9

  • eBook Packages: EngineeringEngineering (R0)

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