矿井粉尘浓度预测模型的建立及应用研究
投稿时间:2019-10-23  修订日期:2020-01-09  点此下载全文
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作者单位E-mail
王月红 华北理工大学 wangyuehong1201@163.com 
高萌 华北理工大学 gaomeng0607@163.com 
赵帅博 华北理工大学  
基金项目:国家自然科学(编号51404086)
中文摘要:为了对矿井粉尘浓度进行准确预测,有效防治矿井粉尘灾害,以某矿粉尘浓度时间序列为基础,提出了差分自回归移动平均预测模型。基于粉尘浓度是非平稳随机数列且ARIMA预测模型可对非平稳数据进行处理的特点,采用SPSS统计分析软件,建立ARIMA粉尘浓度预测模型。首先对粉尘浓度数据进行平稳化处理,根据自相关和偏自相关系数以及BIC准则确定模型参数,初步选定ARIMA(1,2,1)模型,再通过残差自相关和偏自相关函数对模型进行检验,进一步验证了模型的合理性,利用该模型对粉尘浓度进行预测,结果表明:相对误差最大为8.34%,最小为2.4%,相对误差都控制在10%以内,ARIMA模型能够用于矿井粉尘浓度的预测且预测效果较好。
中文关键词:粉尘浓度  时间序列  ARIMA模型  预测
 
Establishment and application of mine dust concentration prediction model
Abstract:In order to predict mine dust concentration accurately and prevent mine dust disaster effectively,this paper puts forward a differential autoregressive moving average prediction model based on a time series of mine dust concentration.Based on the characteristics that the dust concentration is a non-stationary random sequence and the ARIMA prediction model can process non-stationary data, SPSS statistical analysis software is used to establish an ARIMA dust concentration prediction model. Firstly,the dust concentration data should be taken stable processing.Model parameters were determined according to the autocorrelation and partial autocorrelation coefficients and BIC criterion.ARIMA (1,2,1) model was preliminarily selected,and then the model was tested by residual autocorrelation and partial autocorrelation functions,which further verified the rationality of the model.Finally,the model was used to predict the dust concentration.The results show that the relative error is 8.34% at the maximum and 2.4% at the minimum,and all the relative errors are controlled within 10%.ARIMA model can be used to predict mine dust concentration and the prediction effect is good.
keywords:dust concentration  time series  ARIMA model  prediction
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