| 利用优化的组合核相关向量机算法构建地表下沉预测模型 |
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| 引用本文： | 罗亦泳,姚宜斌,赵庆志,周世健.利用优化的组合核相关向量机算法构建地表下沉预测模型[J].武汉大学学报(信息科学版),2018,43(9):1295-1301. |
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| 作者姓名： | 罗亦泳 姚宜斌 赵庆志 周世健 |
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| 作者单位： | 1.武汉大学测绘学院, 湖北 武汉, 430079 |
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| 基金项目： | 国家自然科学基金41374007国家自然科学基金41664001江西省教育厅科技项目GJJ150592江西省数字国土重点实验室开放研究基金DLLJ201612 |
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| 摘 要： | 为了提高地下开采地表下沉预测结果的精度及可靠性，提出了基于混合智能优化算法（hybrid intelligent optimization algorithm，HIOA）与组合核相关向量机（multi-kernel relevance vector machine，MK-RVM）的地下开采地表下沉预测方法。首先，分别构建HIOA与MK-RVM算法，并利用HIOA优化MK-RVM的参数。然后，采用优化后的MK-RVM构建地表下沉几何参数预测模型和动态下沉预测模型。最后，利用以上模型对上山移动角、下山移动角、中心移动角、地表最大下沉及动态下沉进行预测，并分析预测结果的精度及可靠性。实验结果表明，该方法的精度与可靠性较单一核函数相关向量机与支持向量机有较大改善。
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| 关 键 词： | 地表下沉预测 混合智能优化 相关向量机 置信区间 精度分析 |
| 收稿时间： | 2017-06-27 |
Prediction of Surface Subsidence of Underground Mining Based on HIOA and MK-RVM |
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| Institution: | 1.School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China2.Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology, Nanchang 330013, China |
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| Abstract: | In order to improve the accuracy and reliability of surface subsidence prediction of underground mining, a surface subsidence prediction method of underground mining based on HIOA and MK-RVM is proposed. First, the HIOA and MK-RVM algorithms are constructed, and the parameters of MK-RVM are optimized by using HIOA. Then, the prediction model of the surface subsidence geometric parameters and the dynamic subsidence prediction model are constructed based on the optimized MK-RVM. Finally, based on the above model, the rise moving angle, dip moving angle, central moving angle, maximum subsidence and the dynamic subsidence are predicted. The accuracy and reliability of the prediction results are analyzed in order to verify the effectiveness of the proposed method. Experimental results show that the accuracy and reliability of this method are better than single kernel correlation vector machine and support vector machine, and the accuracy and reliability of the new method are excellent. The above analysis confirms the effectiveness of the new method. |
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