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A soft computing fusion for river flow time series forecasting
conference contribution
posted on 2018-01-01, 00:00 authored by Thanh Thi NguyenThanh Thi Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi, Syed Salaken, A KhatamiIn forecasting, the challenge of predicting river flows in time series was amongst the earliest to attract scientific interests. A broad range of mathematical approaches, from simple linear to complex non-linear methods, have been proposed in the literature for this kind of modeling. This paper introduces a hybrid method based on a soft computing fusion for river flow time series forecasting. For the experimental results reported here, this approach consistently outperformed traditional modeling methods. Findings from this specific research promise utility in the water resources and environment sector management where soft computing methods can be applied to various studies for which time series data are available.
History
Event
Fuzzy Systems. Conference (2018 : Rio de Janeiro, Brazil)Pagination
1 - 7Publisher
IEEELocation
Rio de Janeiro, BrazilPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2018-07-08End date
2018-07-13ISBN-13
9781509060207Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2018, IEEETitle of proceedings
FUZZ-IEEE 2018 : IEEE International Conference on Fuzzy SystemsUsage metrics
Keywords
Science & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicComputer ScienceEngineeringsoft computingfuzzy logicstandard additive modelriver flowtime seriesforecastingARTIFICIAL-INTELLIGENCEFUZZY-LOGICNEURAL-NETWORKSRUNOFFMODELSSYSTEMCLASSIFICATIONNeural, Evolutionary and Fuzzy Computation
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