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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主要な著者: | Shabri, Ani, Suhartono, Suhartono |
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フォーマット: | 論文 |
言語: | English |
出版事項: |
Taylor & Francis
2012
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主題: | |
オンライン・アクセス: | 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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