| 基于SVR组合模型的边坡位移预测研究 |
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| 引用本文： | 刘小生,于良,冯腾飞.基于SVR组合模型的边坡位移预测研究[J].金属矿山,2018,47(2):184-187. |
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| 作者姓名： | 刘小生 于良 冯腾飞 |
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| 作者单位： | 江西理工大学建筑与测绘工程学院，江西 赣州 341000 |
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| 基金项目： | 基金项目：国家自然科学基金项目（编号：41561091）。 |
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| 摘 要： | 为解决传统支持向量机预测模型的不足,造成矿山边坡位移预测精度低的问题。提出了一种基于自适应惯性权重PSO算法的支持向量回归机(SVR)组合预测模型。将其运用到某矿边坡滑坡位移预测中,并与基于灰色预测模型、基于传统SVR预测模型预测结果对比,结果表明:基于SVR组合预测模型的矿山边坡位移预测的精度更具优势。
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| 关 键 词： | 支持向量回归机 组合模型 边坡位移预测 |
Slope Displacement Prediction Based on SVR Forecast Combination Model |
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| Liu Xiaosheng,Yu Liang,Feng Tengfei.Slope Displacement Prediction Based on SVR Forecast Combination Model[J].Metal Mine,2018,47(2):184-187. |
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| Authors: | Liu Xiaosheng Yu Liang Feng Tengfei |
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| Affiliation: | School of Architectural and Surveying & Mapping Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China |
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| Abstract: | The shortages existing in prediction model of traditional support vector machine result in the low prediction precision of mine slope displacement.To solve this problem,a kind of combination model based on SVR was put forward and applied to the slope landslide displacement prediction in the mine.The prediction comparison of the combination model with the grey prediction model and the SVR prediction model,shows that the SVR forecast combination model has a more prediction accuracy than other models. |
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| Keywords: | Support vector regression machine Combination model Slope displacement prediction |
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