File(s) under permanent embargo

Water level forecasting using neuro-fuzzy models with local learning

journal contribution
posted on 01.09.2018, 00:00 authored by P K T Nguyen, Lloyd ChuaLloyd Chua, A Talei, Q H Chai
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.

History

Journal

Neural computing and applications

Volume

30

Issue

6

Pagination

1877 - 1887

Publisher

Springer

Location

Berlin, Germany

ISSN

0941-0643

eISSN

1433-3058

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2016, Natural Computing Applications Forum