Settlement Prediction for Mine Heritage Based on Optimized Long Short Term Memory Network
Industrial mine heritage has gradually gained attention for its unique value.In response to the geological hazards of subsidence in mining heritage sites,taking proactive preventive measures is an effective way to reduce losses.We propose a Long Short-Term Memory network(LSTM)integrated with Dung Beetle Optimizer(DBO)to construct a settlement warning model for industrial mine heritage.Selecting the Haizhou open-pit mine in Fuxin City as the experimental site,the small baseline set synthetic aperture radar inter-ferometry(SBAS-InSAR)technology was used to collect settlement data from 55 mining areas.Two denoising methods were used to denoise the collected sample data,and the DBO was applied to optimize the LSTM and establish an industrial mine heritage settlement prediction model.The hyperparameters of the LSTM model were optimized using the DBO to achieve high-precision prediction models,and compared with the model metrics optimized by other algorithms.The experimental results show that the new model has outstanding advantages in predicting the settlement of industrial mine heritage,with low root mean square error 0.045 mm,mean absolute error 0.038 mm and a determination coefficient of 0.956 respectively.It demonstrates high precision,fast convergence and strong stability in predicting the settlement of industrial mine heritage,which provides strong support for the protection of industrial mine heritage.
万方数据
维普
