Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/1279
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Civil and Environmental Engineering | - |
dc.creator | Cheng, C | - |
dc.creator | Lin, J | - |
dc.creator | Sun, Y | - |
dc.creator | Chau, KW | - |
dc.date.accessioned | 2014-12-11T08:24:18Z | - |
dc.date.available | 2014-12-11T08:24:18Z | - |
dc.identifier.isbn | 978-3-540-28320-1 | - |
dc.identifier.uri | http://hdl.handle.net/10397/1279 | - |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartofseries | Lecture notes in computer science ; v. 3162 | - |
dc.rights | © Springer-Verlag Berlin Heidelberg 2005. The original publication is available at http://www.springerlink.com. | en_US |
dc.subject | Adaptive systems | en_US |
dc.subject | Hydropower reservoirs | en_US |
dc.subject | Forecasting reservoir inflow | en_US |
dc.subject | Algorithms | en_US |
dc.subject | Membership functions | en_US |
dc.subject | Reservoirs (water) | en_US |
dc.subject | Manwan Hydropower | en_US |
dc.title | Long-term prediction of discharges in Manwan Hydropower using adaptive-network-based fuzzy inference systems models | en_US |
dc.type | Book Chapter | en_US |
dc.description.otherinformation | Author name used in this publication: Chun-Tian Cheng | en_US |
dc.description.otherinformation | Author name used in this publication: Ying-Guang Sun | en_US |
dc.description.otherinformation | Author name used in this publication: Kwokwing Chau | en_US |
dc.description.otherinformation | Author name used in this publication: Jian-Yi Lin | en_US |
dc.identifier.doi | 10.1007/11539902_145 | - |
dcterms.abstract | Forecasting reservoir inflow is important to hydropower reservoir management and scheduling. An Adaptive-Network-based Fuzzy Inference System (ANFIS) is successfully developed to forecast the long-term discharges in Manwan Hydropower. Using the long-term observations of discharges of monthly river flow discharges during 1953-2003, different types of membership functions and antecedent input flows associated with ANFIS model are tested. When compared to the ANN model, the ANFIS model has shown a significant forecast improvement. The training and validation results show that the ANFIS model is an effective algorithm to forecast the long-term discharges in Manwan Hydropower. The ANFIS model is finally employed in the advanced water resource project of Yunnan Power Group. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | In L Wang, K Chen & YS Ong (Eds.), Advances in natural computation : first international conference, ICNC 2005, Changsha, China, August 27-29, 2005 : proceedings, p. 1152-1161. Berlin ; New York: Springer, 2005 | - |
dcterms.issued | 2005 | - |
dc.identifier.isi | WOS:000232246700145 | - |
dc.identifier.scopus | 2-s2.0-26844569500 | - |
dc.relation.ispartofbook | Advances in natural computation : first international conference, ICNC 2005, Changsha, China, August 27-29, 2005 : proceedings | - |
dc.relation.conference | ICNC | - |
dc.publisher.place | Berlin ; New York | en_US |
dc.identifier.rosgroupid | r21066 | - |
dc.description.ros | 2004-2005 > Academic research: refereed > Publication in refereed journal | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Book Chapter |
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File | Description | Size | Format | |
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LNCS9.pdf | Pre-published version | 199.28 kB | Adobe PDF | View/Open |
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