| 一种基于M估计的Robust-ELM滑坡位移预测方法 |
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| 引用本文： | 高彩云,高宁.一种基于M估计的Robust-ELM滑坡位移预测方法[J].大地测量与地球动力学,2019,39(2):153-157. |
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| 作者姓名： | 高彩云 高宁 |
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| 作者单位： | 河南城建学院测绘与城市空间信息学院,河南省平顶山市龙翔大道,4670362;东华理工大学江西省数字国土重点实验室,南昌市广兰大道418号,330013;河南城建学院测绘与城市空间信息学院,河南省平顶山市龙翔大道,4670362 |
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| 摘 要： | 采用传统ELM算法进行滑坡位移预测时,其网络输出权值由最小二乘估计得出,导致ELM抗差能力较差,从而造成网络训练参数不准确。为此,将M估计与ELM相结合,提出一种基于M估计的RobustELM滑坡变形预测方法。该方法利用加权最小二乘方法来取代最小二乘法计算ELM输出权值,以减少滑坡监测数据中粗差对ELM预测的干扰。分别以链子崖、古树屋滑坡体为例,将Robust-ELM进行了单维、多维粗差的抵御性验证。结果表明,该方法能够有效降低粗差对预测的影响,具有良好的抗差能力。
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| 关 键 词： | 滑坡位移预测 M估计 ELM 粗差 |
A Robust-ELM Approach Based on M Estimation forLandslide Displacement Prediction |
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| GAO Caiyun,GAO Ning.A Robust-ELM Approach Based on M Estimation forLandslide Displacement Prediction[J].Journal of Geodesy and Geodynamics,2019,39(2):153-157. |
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| Authors: | GAO Caiyun GAO Ning |
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| Institution: | (School of Geomatics and Urban Spatial Information,Henan University of Urban Construction,Longxiang Road,Pingdingshan 467036,China;Key Laboratory for Digital Land and Resources of Jiangxi Province,East China Institute of Technology,418 Guanglan Road,Nanchang 330013,China) |
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| Abstract: | In predicting landslide displacement using traditional ELM algorithm, the solution of ELM network output weight parameter is based on least square estimation, leading to poor resistance to gross error of ELM algorithm. To enhance the resistance to gross error of the ELM algorithm, generalized maximum likelihood estimation (M estimation) is integrated with it and M estimation-based Robust-ELM landslide deformation displacement prediction is proposed. Then, the model is applied to predicting the deformation time series data monitored at the Lianziya and Gushuwu landslides. The case studies show that the traditional ELM algorithm is sensitive to gross error in landslide data and has poor resistance to gross error. M estimation-based Robust-ELM algorithm can better resist single and multiple gross errors in landslide data and its prediction accuracy is high. |
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| Keywords: | prediction of landslide displacement M estimation extreme learning machine（EIM） gross error |
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