| 最优权组合预测法在采煤沉陷变形预测中的应用 |
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| 引用本文： | 鲁小红.最优权组合预测法在采煤沉陷变形预测中的应用[J].测绘通报,2020,0(4):111-115. |
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| 作者姓名： | 鲁小红 |
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| 作者单位： | 山西省矿山调查测量队, 山西 太原 030024 |
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| 摘 要： | 煤矿地表沉降变形预测多基于煤矿开采沉陷预计理论展开，基于变形分析理论的变形预测模型目前多集中在单模型预测。本文基于组合预测思想，以非等间隔灰色预测模型与BP神经网络模型为预测单模型，以陕西北部某煤矿采煤工作面上方实测地表沉降值为数据源，以最优加权法对单模型预测结果开展了最优权组合，组合模型中两种单模型的权重分别为0.466 7、0.533 3。选取部分监测点的预测结果进行模型精度评价，结果表明：3种预测模型精度均达到了一级。经对比3种模型预测结果，最优权组合预测的模型精度较单模型明显提升，预测结果较非等间隔灰色预测模型与BP神经网络预测模型有明显增益。
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| 关 键 词： | 沉降变形预测 灰色系统模型 BP神经网络模型 组合预测 最优权组合 |
| 收稿时间： | 2019-12-11 |
| 修稿时间： | 2020-02-29 |
Application of optimal weight combination method in predicting coal mining subsidence deformation |
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| LU Xiaohong.Application of optimal weight combination method in predicting coal mining subsidence deformation[J].Bulletin of Surveying and Mapping,2020,0(4):111-115. |
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| Authors: | LU Xiaohong |
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| Institution: | Shanxi Party of Mine Survey and Measurement, Taiyuan 030024, China |
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| Abstract: | The prediction of ground subsidence and deformation in coal mines mainly based on the theory of subsidence prediction of coal mining. Deformation prediction models based on deformation analysis theory currently focus on single model prediction. Based on the combination forecasting idea, a non-equidistant gray prediction model and a BP neural network model are used as prediction single models. The measured surface settlement value above the coal mining face of a coal mine in northern Shaanxi is used as the data source. The optimal weight combination is used to optimize the prediction results of the single model, and the weights of the two single models in the combined model are 0.466 7 and 0.533 3. The prediction results of some monitoring points are selected to evaluate the model accuracy, and the results show that the accuracy of all three prediction models reached one level. By comparing the prediction results of the three models, the accuracy of the optimal weight combination prediction model is significantly improved compared with the single model, and the prediction results have significant gains compared with the non-equidistant grey prediction model and the BP neural network prediction model. |
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| Keywords: | subsidence deformation prediction grey system model BP neural network model combination forecast optimal weight combination |
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