| 最小二乘支持向量机在太湖流域水质评价中的应用 |
| Application of Least Square-Support Vector Machines in Water Quality Assessment of Taihu Lake Basin |
| 投稿时间：2013-06-02 修订日期：2013-09-03 |
| DOI： |
| 中文关键词:最小二乘支持向量机 太湖流域 水质评价 |
| 英文关键词:Least Square-Support Vector Machines Taihu Lake Basin Water Quality Assessment |
| 基金项目:国家社会科学基金重点项目（10AJY005） |
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| 摘要点击次数: 1832 |
| 全文下载次数: 2013 |
| 中文摘要: |
| 为客观、准确地评价水质状况，从而为水污染防治和水资源合理开发利用提供科学指导，根据最小二乘支持向量机(LS-SVM)的基本原理，引入其分类算法构建太湖流域的水质评价模型，以太湖流域5个重点断面为研究对象，通过对已知训练样本进行学习训练，对测试样本的水质等级进行评价，并将其结果与BP神经网络、判别分析法相比较。结果表明，LS-SVM在太湖流域水质评价方面有着更出色的效果，可为太湖流域水资源管理提供新的参考方法。 |
| 英文摘要: |
| In order to achieve an objective and accurate assessment of water quality and provide a scientific guidante to water pollution prevention and rational utilization of water resources，a model based on LS-SVM was constructed to evaluate the Taihu Lake Basin's water quality，Five monitoring sections in the Taihu Lake Basin were taken as examples. The LS-SVM models were established with the samples which water quality grades were already known，then water quality of testing samples were evaluated by using the well trained LS-SVM models. Further-more，the Back-Propagation Neural Network(BPNN) and Discriminant Analysis were also used with the same testing samples to testify the method's efficiency and accuracy. The results comparison of three methods showed that the LS-SVM method presented in this paper performed much better than BPNN and Discriminant Analysis Method in the water quality assessment of the Taihu Lake Basin. In conclusion，the LS-SVM might be a new reference method for the Taihu Lake Basin's water resources management.
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| 陈曦,仇 蕾,黄泽元.2013.最小二乘支持向量机在太湖流域水质评价中的应用[J].水生态学杂志,34(6):16-21. |
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