Streamflow forecasting using least-squares support vector machines
This paper investigates the ability of a least-squares support vector machine (LSSVM) model to improve the accuracy of streamflow forecasting. Cross-validation and grid-search methods are used to automatically determine the LSSVM parameters in the forecasting process. To assess the effectiveness of...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis
2012
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/33550/1/AniShabri2012_StreamflowForecastingusingLeastSquares.pdf http://eprints.utm.my/id/eprint/33550/ http://dx.doi.org/10.1080/02626667.2012.714468 |
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| Institution: | Universiti Teknologi Malaysia |
| Language: | English |