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基于组合预测模型的露天矿高陡边坡滑坡变形研究
引用本文:肖海平,杨旺生,肖岚,郭钟群,曹希西. 基于组合预测模型的露天矿高陡边坡滑坡变形研究[J]. 金属矿山, 2014, 43(4): 169-171
作者姓名:肖海平  杨旺生  肖岚  郭钟群  曹希西
作者单位:1.江西理工大学建筑与测绘工程学院,江西 赣州 341000;2.东华理工大学勘测设计研究院于都测绘分院,江西 赣州 341000;3.赣州市国土局测绘科,江西 赣州 341000
基金项目:* 江西省教育厅科技计划项目(编号:GJJ11472),江西理工大学科研基金项目(编号:jxxj12013)。
摘    要:随着露天矿山开采规模及深度的不断扩大,致使矿山边坡变陡、变高,而形成高陡边坡,但由于地质条件以及矿山施工等因素的影响,高陡边坡经常发生崩塌、滑坡等比较严重的地质灾害。因此,为加强对露天矿山高陡边坡的变形预测,应及时研究滑坡的变化趋势,开展滑坡预警,指导矿山生产,保障人民的生命财产安全。建立了一种能够更加有效地反映出变形体变化趋势的组合预测模型,并介绍了其建模思想及计算方法,在此基础上,依据某高陡边坡实际监测数据,将其预测结果与独立预测模型的预测结果进行分析比较,结果显示,其精度要优于独立预测模型的精度,且可靠性更高,具有较强的适用性。

关 键 词:组合预测模型  灰色模型  线性回归  神经网络  

Research on Landslide Deformation of High and Steep Slope in Open-pit Mine based on Combination Prediction Model
Xiao Haiping,Yang Wangsheng,Xiao Lan,Guo Zhongqun,Cao Xixi. Research on Landslide Deformation of High and Steep Slope in Open-pit Mine based on Combination Prediction Model[J]. Metal Mine, 2014, 43(4): 169-171
Authors:Xiao Haiping  Yang Wangsheng  Xiao Lan  Guo Zhongqun  Cao Xixi
Affiliation:1.School of Architectural and Surveying & Mapping Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China;2.Yudu Branch of School of Surveying and Design,East China Institute of Technology,Ganzhou 341000,China;3.Department of Surveying,Ganzhou Land and Resources Bureau,Ganzhou 341000,China
Abstract:In order to strengthen the deformation prediction of high and steep slope in open-pit mine,and timely obtain the trend of the landslide,the landslide pre-warning was conducted,which guided the mine production and ensured the safety of people′s life and property.A combined prediction model was established,and the modeling ideas and calculating methods were introduced in the paper.On this basis,according to the actual monitoring data of a high and steep slope,the predicted results were analyzed and compared with the predictions of the independent model.The results showed that the precision of the combined model is superior to that of the independent model with higher reliability.Therefore,it owns strong applicability.
Keywords:Combined prediction model  Grey model  Linear regression model  Neural network
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