Mining Subsidence Prediction Based on Improved GM(1,1)Model by Total Least Squares Method
[Purposes]This study proposes the use of Total Least Squares(TLS)in combination with the GM(1,1)model to conduct experimental research on the difficult-to-predict surface subsidence in coal mining areas affected by various factors.[Methods]Specifically,this study is based on the subsidence monitoring data from a certain mining area between 2011 and 2017.The GM(1,1)model is used in con-junction with both the Total Least Squares(TLS)and the Ordinary Least Squares(LS)methods for predic-tive experiments.[Findings]The experimental results indicate that,based on the GM(1,1)prediction model,the use of the TLS method improves the accuracy of subsidence monitoring predictions in the min-ing area by 0.49 mm for the year 2018 and by 0.55 mm for the year 2019 compared to the LS method.[Conclusions]Therefore,this study confirms that the GM(1,1)model using the TLS method provides higher accuracy and better performance in predicting ground subsidence in mining areas compared to the GM(1,1)model using the LS method.
subsidence in mining areasTotal Least Squares methodGM(1,1)modelprediction accuracy
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