地表沉陷预测的改进BP神经网络模型  

Improved BP Neural Network Model for Surface Subsidence Prediction

作　　者：姜燕 连晗[1] 席东河[1] JIANG Yan;LIAN Han;XI Donghe(School of Electronic Information Engineering,Henan Polytechnic Institute,Nanyang 473000,China)

机构地区：[1]河南工业职业技术学院电子信息工程学院,河南南阳473000

出　　处：《金属矿山》2024年第2期205-211,共7页Metal Mine

基　　金：河南省科技攻关项目(编号:172102210127)。

摘　　要：为了更加准确地预测地表沉陷变形,基于Adaboost算法采用多网络共同计算策略改进了BP神经网络,通过实际沉降数据对Adaboost算法改进后的神经网络进行训练,预测地表最大下沉量、影响角正切和拐点偏移距,将预测的3个参数代入概率积分法中,建立了地表沉陷公式,对改进效果和地表沉陷公式分别进行了验证。结果表明:(1)通过对比改进前后BP神经网络的计算精度,未经过Adaboost算法改进的BP神经网络误差明显大于改进后的BP神经网络,说明基于Adaboost修正后的BP神经网络计算精度得到了有效提升;(2)基于BP神经网络对最大下沉量、影响角正切和拐点偏移距3个参数进行预测,结合概率分析法,能够实现稳沉后采空区主断面上方地表沉降规律的准确描述。以鲁西南地区某矿3301采空区地表为例,利用改进BP神经网络预测了地表最大下沉量、影响角正切和拐点偏移距,进而给出了地表沉陷曲线,与现场实测结果对比显示:改进BP神经网络的最大误差小于0.105 m,最大相对误差为4.3%,证明了所提计算方法的可靠性。In order to predict surface subsidence deformation more accurately,a multi network collaborative calculation strategy was adopted based on the Adaboost algorithm to improve the BP neural network.The improved neural network was trained on actual subsidence data to predict the maximum subsidence,influence angle tangent,and inflection point offset.The three predicted parameters were introduced into the probability integration method,and a surface subsidence formula was estab-lished.The improvement effect and surface subsidence formula were verified separately.The study results show that:①By comparing the calculation accuracy of the BP neural network before and after improvement,the results showed that the error of the BP neural network without Adaboost algorithm improvement it is obviously greater than the improved BP neural network,in-dicating that the calculation accuracy of the BP neural network based on Adaboost correction has been effectively improved;②Based on the BP neural network,the maximum subsidence,influence angle tangent,and inflection point offset are predicted.Combined with probability analysis method,the description of surface subsidence above the main section of the goaf after stable subsidence can be achieved.Taking the surface of the 3301 goaf in a certain mine in Southwestern Shandong Province as the study example,the improved BP neural network is used to predict the maximum subsidence,influence angle tangent,and inflec-tion point offset,and then the surface subsidence curve is given.Compared with the on-site measurement results,the comparison results show that the maximum error of the improved BP neural network is less than 0.105 m,and the maximum relative error is 4.3%,which proves the reliability of the calculation method in this paper.

关 键 词：地表沉陷 BP神经网络 采空区 ADABOOST算法 误差分析 

分 类 号：TD325[矿业工程—矿井建设]