Water level forecasting using neuro-fuzzy models with local learning

Phuoc Khac Tien Nguyen, Lloyd Hock Chye Chua, Amin Talei, Quek Hiok Chai

    Research output: Contribution to journalArticleResearchpeer-review

    8 Citations (Scopus)


    The global learning method is widely used to train data-driven models for hydrological forecasting. The drawback of global models is that a long data record is required and the model is not easily adapted once it is trained. This study investigated the local learning approach applied in the dynamic evolving neural-fuzzy inference system (DENFIS) to provide 5-lead-day water level forecasts for the Mekong River. The local learning method focuses on the relationship between input and output variables at the most recent state. The results obtained from DENFIS were found to be better than results obtained from adaptive neuro-fuzzy inference system, which uses global learning approach, and the unified river basin simulator model. Local learning provides continuous model updating, and the results obtained in this study show that local learning is a promising tool for water level forecasting in real-time flood warning applications.

    Original languageEnglish
    Pages (from-to)1877-1887
    Number of pages11
    JournalNeural Computing and Applications
    Issue number6
    Publication statusPublished - Sep 2018


    • Forecast
    • Global learning
    • Local learning
    • Neuro-fuzzy model
    • Water level

    Cite this