Rainfall-runoff modeling using dynamic evolving neural fuzzy inference system with online learning

Kwin, Chang Tak, Talei, Amin, Alaghmand, Sina and Chua, Lloyd H. C. 2016, Rainfall-runoff modeling using dynamic evolving neural fuzzy inference system with online learning, Procedia engineering, vol. 154, pp. 1103-1109, doi: 10.1016/j.proeng.2016.07.518.

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Title Rainfall-runoff modeling using dynamic evolving neural fuzzy inference system with online learning
Author(s) Kwin, Chang Tak
Talei, Amin
Alaghmand, Sina
Chua, Lloyd H. C.ORCID iD for Chua, Lloyd H. C. orcid.org/0000-0003-2523-3735
Journal name Procedia engineering
Volume number 154
Start page 1103
End page 1109
Total pages 7
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016
ISSN 1877-7058
Keyword(s) rainfall-runoff modeling
neuro-fuzzy systems
Summary This is an open access article under the CC BY-NC-ND license.Neuro-Fuzzy Systems (NFS) are computational intelligence tools that have recently been employed in hydrological modeling. In many of the common NFS the learning algorithms used are based on batch learning where all the parameters of the fuzzy system are optimized off-line. Although these models have frequently been used, there is a criticism on such learning process as the number of rules are needed to be predefined by the user. This will reduce the flexibility of the NFS architecture while dealing with different data with different level of complexity. On the other hand, online or local learning evolves through local adjustments in the model as new data is introduced in sequence. In this study, dynamic evolving neural fuzzy inference system (DENFIS) is used in which an evolving, online clustering algorithm called the Evolving Clustering Method (ECM) is implemented. ECM is an online, maximum distance-based clustering method which is able to estimate the number of clusters in a data set and find their current centers in the input space through its fast, one-pass algorithm. The 10-minutes rainfall-runoff time series from a small (23.22 km2) tropical catchment named Sungai Kayu Ara in Selangor, Malaysia, was used in this study. Out of the 40 major events, 12 were used for training and 28 for testing. Results obtained by DENFIS were then compared with the ones obtained by physically-based rainfall-runoff model HEC-HMS and a regression model ARX. It was concluded that DENFIS results were comparable to HEC-HMS and superior to ARX model. This indicates a strong potential for DENFIS to be used in rainfall-runoff modeling.
Notes This paper was originally presented at the 12th International Conference of Hydroinformatics (HIC 2016) - Smart water fro the future
Language eng
DOI 10.1016/j.proeng.2016.07.518
Field of Research 091006 Manufacturing Processes and Technologies (excl Textiles)
MD Multidisciplinary
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30090302

Document type: Journal Article
Collection: School of Engineering
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