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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| Veröffentlicht in: | Water resources management Jg. 29; H. 14; S. 5109 - 5127 |
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01.11.2015
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| Abstract | 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 ahead streamflow forecasting by using cross-validation method. Monthly streamflow data belonging to two stations, Besiri Station on Garzan Stream and Baykan Station on Bitlis Stream, in Dicle Basin of Turkey were used. The LSSVR and ANFIS-FCM results were compared with autoregressive moving average (ARMA) models. It was found that the LSSVR models performed better than the ANFIS-FCM and ARMA models in 1-month ahead streamflow forecasting. The ANFIS-FCM models are also found to be better than the ARMA models. The effect of periodicity on forecasting performance of the LSSVR models was also investigated. Adding periodicity component as input to the LSSVR models significantly improved the models’ accuracy in forecasting. In the second part of the study, the accuracy of the LSSVR and ANFIS-FCM models was tested in streamflow estimation using data from nearby stream. Based on the results, the LSSVR was found to be better than the ANFIS-FCM and successfully used in estimating monthly streamflows by using nearby station data. |
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| AbstractList | 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 ahead streamflow forecasting by using cross-validation method. Monthly streamflow data belonging to two stations, Besiri Station on Garzan Stream and Baykan Station on Bitlis Stream, in Dicle Basin of Turkey were used. The LSSVR and ANFIS-FCM results were compared with autoregressive moving average (ARMA) models. It was found that the LSSVR models performed better than the ANFIS-FCM and ARMA models in 1-month ahead streamflow forecasting. The ANFIS-FCM models are also found to be better than the ARMA models. The effect of periodicity on forecasting performance of the LSSVR models was also investigated. Adding periodicity component as input to the LSSVR models significantly improved the models’ accuracy in forecasting. In the second part of the study, the accuracy of the LSSVR and ANFIS-FCM models was tested in streamflow estimation using data from nearby stream. Based on the results, the LSSVR was found to be better than the ANFIS-FCM and successfully used in estimating monthly streamflows by using nearby station data. |
| Author | Kisi, Ozgur |
| Author_xml | – sequence: 1 givenname: Ozgur surname: Kisi fullname: Kisi, Ozgur email: okisi@basari.edu.tr organization: Architectural and Engineering Faculty, Civil Engineering Department, Canik Basari University |
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| Snippet | This paper investigates the ability of least square support vector regression (LSSVR) and adaptive neuro-fuzzy embedded fuzzy c-means clustering (ANFIS-FCM) in... |
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| SubjectTerms | 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 |
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| Title | Streamflow Forecasting and Estimation Using Least Square Support Vector Regression and Adaptive Neuro-Fuzzy Embedded Fuzzy c-means Clustering |
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