Rainfall-runoff Modeling Using Dynamic Evolving Neural Fuzzy Inference System with Online Learning

Chang Tak Kwin, Amin Talei, Sina Alaghmand, Lloyd H.C. Chua

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    12 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publication12th International Conference on Hydroinformatics, HIC 2016
    Subtitle of host publicationIncheon, South Korea, 21-26 August 2016
    EditorsJoong Hoon Kim, Hung Soo Kim, Do Guen Yoo, Donghwi Jung, Chang Geun Song
    PublisherElsevier
    Pages1103-1109
    Number of pages7
    DOIs
    Publication statusPublished - 2016
    EventInternational Conference on Hydroinformatics 2016 - Songdo ConvensiA, Incheon, Korea, Republic of (South)
    Duration: 21 Aug 201626 Aug 2016
    Conference number: 12th
    http://www.hic2016.org/html/index.php

    Publication series

    NameProcedia Engineering
    PublisherElsevier BV
    Volume154
    ISSN (Electronic)1877-7058

    Conference

    ConferenceInternational Conference on Hydroinformatics 2016
    Abbreviated titleHIC 2016
    CountryKorea, Republic of (South)
    CityIncheon
    Period21/08/1626/08/16
    OtherSmart Water for the Future
    Internet address

    Keywords

    • ARX
    • DENFIS
    • HEC-HMS
    • Neuro-fuzzy systems
    • Rainfall-runoff modeling

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