| 基于PCA-SVM的边坡稳定性预测模型研究 |
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| 引用本文： | 丁宏飞,朱炯,罗书学.基于PCA-SVM的边坡稳定性预测模型研究[J].路基工程,2011,0(2):5-7. |
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| 作者姓名： | 丁宏飞 朱炯 罗书学 |
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| 作者单位： | 1.西南交通大学智能控制开发中心, 成都 610031 |
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| 摘 要： | 提出了一种基于主成分分析(PCA)和支持向量机(SVM)的边坡稳定性预测模型。首先分析了影响边坡稳定性的因素,采用主成分分析方法求取主成分;再将主成分作为输入对支持向量机进行训练,并利用遗传算法优化支持向量机参数;最后通过实例与常用寻参方法所得结果进行比较。结果表明,该法能减少输入变量维数,提高了边坡工程稳定性的预测精度。
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| 关 键 词： | 主成分分析 支持向量机 遗传算法 边坡稳定性 |
| 收稿时间： | 2019-11-06 |
Study on Prediction Model of Slope Stability Based on PCA-SVM |
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| Authors: | DING Hong-fei ZHU Jiong LUO Shu-xue |
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| Affiliation: | 1.Intelligent Control Development Center,Southwest Jiaotong University,Chengdu 610031,China;2.School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,China) |
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| Abstract: | A prediction model of slope stability based on principal component analysis（PCA） and support vector machine（SVM） is put forward.First,the influencing factors of slope stability are analyzed and the principal component is searched through principal component analysis.Then support vector machine is trained with principal component as the input and the parameters of support vector machine are optimized using genetic algorithm.Finally the result from the common search method is compared with engineering example.It is indicated that this method can reduce input variable dimension to improve the precision of prediction of slope stability. |
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| Keywords: | principal component analysis support vector machine genetic algorithm slope stability |
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