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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Bibliographic Details
Published in:Water resources management Vol. 29; no. 14; pp. 5109 - 5127
Main Author: Kisi, Ozgur
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.11.2015
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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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ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-015-1107-7