Water Quantity Prediction Using Least Squares Support Vector Machines (LS-SVM) Method
The impact of reliable estimation of stream flows at highly urbanized areas and the associated receiving waters is very important for water resources analysis and design. We used the least squares support vector machine (LS-SVM) based algorithm to forecast the future streamflow discharge. A Gaussian...
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International Institute of Informatics and Cybernetics
2014-08-01
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doaj-4ebcab3506f04598b2e232a3257c06b02020-11-24T21:43:33ZengInternational Institute of Informatics and CyberneticsJournal of Systemics, Cybernetics and Informatics1690-45242014-08-011245358Water Quantity Prediction Using Least Squares Support Vector Machines (LS-SVM) MethodNian Zhang0Charles Williams1Pradeep Behera2 The impact of reliable estimation of stream flows at highly urbanized areas and the associated receiving waters is very important for water resources analysis and design. We used the least squares support vector machine (LS-SVM) based algorithm to forecast the future streamflow discharge. A Gaussian Radial Basis Function (RBF) kernel framework was built on the data set to optimize the tuning parameters and to obtain the moderated output. The training process of LS-SVM was designed to select both kernel parameters and regularization constants. The USGS real-time water data were used as time series input. 50% of the data were used for training, and 50% were used for testing. The experimental results showed that the LS-SVM algorithm is a reliable and efficient method for streamflow prediction, which has an important impact to the water resource management field.http://www.iiisci.org/Journal/CV$/sci/pdfs/SA145GH14.pdf Least Squares Support vector MachineWater Quantity Prediction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nian Zhang Charles Williams Pradeep Behera |
spellingShingle |
Nian Zhang Charles Williams Pradeep Behera Water Quantity Prediction Using Least Squares Support Vector Machines (LS-SVM) Method Journal of Systemics, Cybernetics and Informatics Least Squares Support vector Machine Water Quantity Prediction |
author_facet |
Nian Zhang Charles Williams Pradeep Behera |
author_sort |
Nian Zhang |
title |
Water Quantity Prediction Using Least Squares Support Vector Machines (LS-SVM) Method |
title_short |
Water Quantity Prediction Using Least Squares Support Vector Machines (LS-SVM) Method |
title_full |
Water Quantity Prediction Using Least Squares Support Vector Machines (LS-SVM) Method |
title_fullStr |
Water Quantity Prediction Using Least Squares Support Vector Machines (LS-SVM) Method |
title_full_unstemmed |
Water Quantity Prediction Using Least Squares Support Vector Machines (LS-SVM) Method |
title_sort |
water quantity prediction using least squares support vector machines (ls-svm) method |
publisher |
International Institute of Informatics and Cybernetics |
series |
Journal of Systemics, Cybernetics and Informatics |
issn |
1690-4524 |
publishDate |
2014-08-01 |
description |
The impact of reliable estimation of stream flows at highly urbanized areas and the associated receiving waters is very important for water resources analysis and design. We used the least squares support vector machine (LS-SVM) based algorithm to forecast the future streamflow discharge. A Gaussian Radial Basis Function (RBF) kernel framework was built on the data set to optimize the tuning parameters and to obtain the moderated output. The training process of LS-SVM was designed to select both kernel parameters and regularization constants. The USGS real-time water data were used as time series input. 50% of the data were used for training, and 50% were used for testing. The experimental results showed that the LS-SVM algorithm is a reliable and efficient method for streamflow prediction, which has an important impact to the water resource management field. |
topic |
Least Squares Support vector Machine Water Quantity Prediction |
url |
http://www.iiisci.org/Journal/CV$/sci/pdfs/SA145GH14.pdf
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work_keys_str_mv |
AT nianzhang waterquantitypredictionusingleastsquaressupportvectormachineslssvmmethod AT charleswilliams waterquantitypredictionusingleastsquaressupportvectormachineslssvmmethod AT pradeepbehera waterquantitypredictionusingleastsquaressupportvectormachineslssvmmethod |
_version_ |
1725913428313243648 |