Comparative Prediction of Surface Settlement in Coal Pillar Replacement Mining Based on EFA-tSSA-SVR Model Study

Abstract

Abstract In order to accurately predict it, the maximum subsidence value of the surface of coal pillar replacement fill mining is examined using machine learning and numerical simulation. Exploratory factor analysis (EFA) is utilized to achieve the dimensionality reduction of influencing factors after carefully considering the seven key influencing factors. The findings of the numerical simulation prediction are then compared with the support vector regression machine model (EFA-tSSA-SVR), which is trained on various data sets and optimized using the enhanced sparrow search algorithm (tSSA). The surface subsidence in the first stage is calculated by the numerical model to be 2.00mm, and in the second stage, it is calculated to be 28.00mm, both of which are within 11.10% of the actual amount. The latter forecasts a relative inaccuracy that is about twice as large as the former. The findings show that the EFA-tSSA-SVR optimization model fits well, with an R2 close to 1, and a predicted value of 26.40mm that is very close to the measured value of 25.20mm with a 5.90% error. The EFA-tSSA-SVR model can predict the maximum amount of surface sinking, and both models can provide reference values for surface subsidence prediction.