Hydrological modelling with a dynamic neural fuzzy inference system Talei, Amin, Chua, Lloyd H.C. and Quek, Chai 2012, Hydrological modelling with a dynamic neural fuzzy inference system, in HIC 2012 : Understanding changing climate and environment and finding solutions : Proceedings of the 10th International Conference on Hydroinformatics, [The Conference], Hamburg, Germany, pp. 1-1. Attached Files Name Description MIMEType Size Downloads Title Hydrological modelling with a dynamic neural fuzzy inference system Author(s) Talei, Amin Chua, Lloyd H.C. orcid.org/0000-0003-2523-3735 Quek, Chai Conference name International Conference on Hydroinformatics (10th : 2012 : Hamburg, Germany) Conference location Hamburg, Germany Conference dates 14-18 Jul. 2012 Title of proceedings HIC 2012 : Understanding changing climate and environment and finding solutions : Proceedings of the 10th International Conference on Hydroinformatics Editor(s) [Unknown] Publication date 2012 Series International Conference on Hydroinformatics Start page 1 End page 1 Total pages 1 Publisher [The Conference] Place of publication Hamburg, Germany Summary Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) is a Takagi-Sugeno-type fuzzy inference system for online learning which can be applied for dynamic time series prediction. Data from Heshui catchment (2,275 km2) which is rural catchment in China, comprising daily time series of rainfall and discharge from January 1, 1990 to January 21, 2006 were analyzed. Rainfall and discharge antecedents were the inputs used for the DENFIS and ANFIS models and the output was discharge at the present time. DENFIS model results were compared with the results obtained from the physically-based University Regina Hydrologic Model (URHM) and an Adaptive Network-based Fuzzy Inference System (ANFIS) which employs offline learning. Our analysis shows that DENFIS results are better or at least comparable to URHM, but almost identical to ANFIS. Language eng Field of Research 059999 Environmental Sciences not elsewhere classified Socio Economic Objective 970105 Expanding Knowledge in the Environmental Sciences HERDC Research category E2.1 Full written paper - non-refereed / Abstract reviewed Copyright notice ©2012, International Conference on Hydroinformatics (HIC) Persistent URL http://hdl.handle.net/10536/DRO/DU:30063685 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Engineering   Link to Related Work Hydrological modelling with a dynamic neural fuzzy inference system (deposited 09-12-2021) International Conference on Hydroinformatics (HIC) Publisher (deposited 11-06-2014)   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 29 May 2014, 14:47:52 EST Tue, 03 Jun 2014, 10:55:08 EST Wed, 11 Jun 2014, 11:07:01 EST Wed, 11 Jun 2014, 13:43:48 EST Fri, 22 Aug 2014, 16:26:24 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 77 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Thu, 29 May 2014, 14:47:52 EST