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Hydrological modelling with a dynamic neural fuzzy inference system

conference contribution
posted on 2012-01-01, 00:00 authored by A Talei, Lloyd ChuaLloyd Chua, C Quek
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.

History

Event

International Conference on Hydroinformatics (10th : 2012 : Hamburg, Germany)

Series

International Conference on Hydroinformatics

Pagination

1 - 1

Publisher

[The Conference]

Location

Hamburg, Germany

Place of publication

Hamburg, Germany

Start date

2012-07-14

End date

2012-07-18

Language

eng

Publication classification

E2.1 Full written paper - non-refereed / Abstract reviewed

Copyright notice

2012, International Conference on Hydroinformatics (HIC)

Title of proceedings

HIC 2012 : Understanding changing climate and environment and finding solutions : Proceedings of the 10th International Conference on Hydroinformatics

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