You are not logged in.

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.
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
Collection: School of Engineering
Connect to link resolver
 
Link to Related Work
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 51 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Thu, 29 May 2014, 14:47:52 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.