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。
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.
作者
鲁小红
LU Xiaohong(Shanxi Party of Mine Survey and Measurement,Taiyuan 030024,China)
出处
《测绘通报》
CSCD
北大核心
2020年第4期111-115,共5页
Bulletin of Surveying and Mapping
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
subsidence deformation prediction
grey system model
BP neural network model
combination forecast
optimal weight combination
