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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23264

Title: Soft Computing Approaches for Urban Water Demand Forecasting
Authors: Kokkinos, Konstantinos
Papageorgiou, Elpiniki
Poczeta, Katarzyna
Papadopoulos, Lefteris
Laspidou, Chrysi
Issue Date: 2016
Publisher: Springer International Publishing
Citation: Czarnowski, I.; Caballero, A.M.; Howlett, R.J.; Jain, L.C. (Ed.). Intelligent Decision Technologies 2016: Proceedings of the 8th KES International Conference on Intelligent Decision Technologies (KES-IDT 2016) – Part II, Springer International Publishing,p. 357-367
Series/Report: Smart Innovation Systems and Technologies
Series/Report no.: 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.
Notes: [Kokkinos, Konstantinos; Papadopoulos, Lefteris] Inst Informat Technol, CERTH, 6th Km Charilaou Thermi Rd, Thermi 57001, Greece. [Papageorgiou, Elpiniki I.] Hasselt Univ, Fac Business Econ, Hasselt, Belgium. [Poczeta, Katarzyna] Kielce Univ Technol, Dept Informat Syst, Al Tysiaclecia Panstwa Polskiego 7, PL-25314 Kielce, Poland. [Laspidou, Chrysi] Univ Thessaly, Dept Civil Engn, Nea Ionia, Greece.
URI: http://hdl.handle.net/1942/23264
DOI: 10.1007/978-3-319-39627-9_31
ISI #: 000389636900031
ISBN: 9783319396262
ISSN: 2190-3018
Category: C1
Type: Proceedings Paper
Validation: ecoom, 2018
Appears in Collections: Research publications

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