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Soft computing approaches for urban water demand forecasting
dc.creator | Kokkinos K., Papageorgiou E.I., Poczeta K., Papadopoulos L., Laspidou C. | en |
dc.date.accessioned | 2023-01-31T08:43:32Z | |
dc.date.available | 2023-01-31T08:43:32Z | |
dc.date.issued | 2016 | |
dc.identifier | 10.1007/978-3-319-39627-9_31 | |
dc.identifier.isbn | 9783319396262 | |
dc.identifier.issn | 21903018 | |
dc.identifier.uri | http://hdl.handle.net/11615/74954 | |
dc.description.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. © Springer International Publishing Switzerland 2016. | en |
dc.language.iso | en | en |
dc.source | Smart Innovation, Systems and Technologies | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84977136747&doi=10.1007%2f978-3-319-39627-9_31&partnerID=40&md5=70e0a5627309ab8e5ff8d0e2cdf04d79 | |
dc.subject | Artificial intelligence | en |
dc.subject | Cognitive systems | en |
dc.subject | Decision support systems | en |
dc.subject | Forecasting | en |
dc.subject | Fuzzy inference | en |
dc.subject | Fuzzy rules | en |
dc.subject | Fuzzy systems | en |
dc.subject | Large scale systems | en |
dc.subject | Soft computing | en |
dc.subject | Software architecture | en |
dc.subject | Software design | en |
dc.subject | Water distribution systems | en |
dc.subject | Water management | en |
dc.subject | Water resources | en |
dc.subject | Water supply | en |
dc.subject | Water supply systems | en |
dc.subject | Decision support system (dss) | en |
dc.subject | Decision supports | en |
dc.subject | Fuzzy cognitive map | en |
dc.subject | Model driven architectures | en |
dc.subject | Neuro-Fuzzy | en |
dc.subject | Soft computing approaches | en |
dc.subject | Spatio-temporal models | en |
dc.subject | Water resources management | en |
dc.subject | Decision making | en |
dc.subject | Springer Science and Business Media Deutschland GmbH | en |
dc.title | Soft computing approaches for urban water demand forecasting | en |
dc.type | conferenceItem | en |
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