Prediction of Residual Subsidence in Mining Area Based on Verhulst Model Optimized by PSO Algorithm
The residual subsidence caused by mining has a long stabilization time and great potential harm.It is necessary to accu-rately predict the residual subsidence value of the ground of the mining area.In view of the large modeling error and weak applicability of the traditional residual subsidence Verhulst model,the first data of the data sequence was kept unchanged in the modeling process,which leads to the poor prediction effect.Based on the direct discrete Verhulst model,the PSO(particle swarm optimization)algorithm was introduced to find the optimal solution of the initial value of the model iteration,and the direct discrete Verhulst model of mining residual subsidence based on PSO was established.The surface residual subsidence monitoring data sets of two time scales in Yangquan,Shanxi and Yanzhou,Shandong were used for example verification.Finally,the visualization of the model algorithm was realized by using Matlab App Designer tool.The results show that the prediction accuracy and stability gain of residual subsidence in mining area based on direct discrete Verhulst model optimized by particle swarm optimization are obvious,and the developed calcula-tion tool is correct and effective.
residual subsidenceVerhulst modelPSOsubsidence predictionMATLABsoftware development
NETL
NSTL
万方数据
