| 基于支持向量机的中长期入库径流预报 |
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| 引用本文： | 赵红标,吴义斌.基于支持向量机的中长期入库径流预报[J].红水河,2009,28(5):55-59. |
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| 作者姓名： | 赵红标 吴义斌 |
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| 作者单位： | 龙滩水电开发有限公司,广西,南宁,530000 |
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| 基金项目： | 科技部重大基础研究前期研究专项自助项目(2004CCA02500) |
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| 摘 要： | 采用基于支持向量机的预测模型对水库中长期入库径流进行预报,建立径流预报的SVM模型。预报因子的优劣决定着预测精度的高低。为了提高预报精度,尝试采用模糊优选法对预报因子进行优选。将所建模型应用于新疆雅马渡站的径流预测中,并与没有进行预报因子优选的SVM模型进行比较。结果表明,进行预报因子优化后的SVM模型明显提高了径流的预报精度,具有更好的应用价值。
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| 关 键 词： | 支持向量机（SVM） 径流预报 预报因子 |
Long-term Runoff Forecast Based on Support Vector Machine |
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| ZHAO Hong-biao,WU Yi-bin.Long-term Runoff Forecast Based on Support Vector Machine[J].Hongshui River,2009,28(5):55-59. |
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| Authors: | ZHAO Hong-biao WU Yi-bin |
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| Affiliation: | Longtan Hydropower Development Limited Liability Company;Nanning 530000 |
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| Abstract: | Forecast model based on the support vector machine is used to forecast long-term runoff of the reservoir,and the SVM runoff forecast model is established.The nature of forecast factors determines the level of forecast accuracy.In order to improve forecast accuracy,the fuzzy optimization method is used to optimize forecast factors.The model is applied to the runoff forecast of Yamadu station in Xinjiang,and is compared with the SVM model that does not optimize forecast factors.The results show that the SVM m... |
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