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Water level forecasting using neuro-fuzzy models with local learning
journal contribution
posted on 2018-09-01, 00:00 authored by P K T Nguyen, Lloyd ChuaLloyd Chua, A Talei, Q H ChaiThe 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 applicationsVolume
30Issue
6Pagination
1877 - 1887Publisher
SpringerLocation
Berlin, GermanyPublisher DOI
ISSN
0941-0643eISSN
1433-3058Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2016, Natural Computing Applications ForumUsage metrics
Keywords
neuro-fuzzy modelforecastlocal learningwater levelglobal learningScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer ScienceRESOURCES APPLICATIONSINPUT DETERMINATIONINFERENCE SYSTEMNETWORK MODELSPREDICTIONRAINFALLRIVERDISCHARGEALGORITHMArtificial Intelligence and Image Processing
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