Streamflow Forecasting and Estimation Using Least Square Support Vector Regression and Adaptive Neuro-Fuzzy Embedded Fuzzy c-means Clustering

This paper investigates the ability of least square support vector regression (LSSVR) and adaptive neuro-fuzzy embedded fuzzy c-means clustering (ANFIS-FCM) in forecasting and estimation of monthly streamflows. In the first part of the study, the LSSVR and ANFIS-FCM models were tested in 1-month ahe...

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Vydáno v:	Water resources management Ročník 29; číslo 14; s. 5109 - 5127
Hlavní autor:	Kisi, Ozgur
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.11.2015 Springer Nature B.V
Témata:	Accuracy Atmospheric Sciences basins Civil Engineering Creeks & streams Dams Earth and Environmental Science Earth Sciences Enhanced oil recovery Environment Estimating techniques Forecasting Fuzzy Fuzzy logic Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hydrologic data Hydrology/Water Resources Least squares method Mathematical models periodicity Precipitation Rain Rivers Stations Stream discharge Stream flow Streamflow forecasting Streams Studies Water resources Water resources management Water runoff MED, Turkey Least square support vector regression Streamflow Forecasting Estimation
ISSN:	0920-4741, 1573-1650
On-line přístup:	Získat plný text
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