Surface stability is essential in underground mines health management systems. Unexpected Surface displacement in underground mines could lead to loss of lives, injuries, and economic losses. To reduce or neutralise t...Surface stability is essential in underground mines health management systems. Unexpected Surface displacement in underground mines could lead to loss of lives, injuries, and economic losses. To reduce or neutralise the adverse effects of surface displacement, it is vital to monitor and accurately predict them and unravel their mechanisms. In recent years, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) have proven effective in predicting complex problems. However, CNN neglects the dynamic dependency of the input in the temporal dimension, which affects surface displacement features. The Convolutional-LSTM model can dynamically learn the temporal dependency among input features via the feedback connections in the LSTM to improve accurate captures of surface displacement features. This study focused on evaluating the C-LSTM model in predicting surface displacement of underground mines and assessed the predictive capabilities and generalisation strength of using hybridised ANN models. Geodetic and geotechnical data gathered from a Gold Mine in Ghana was used. The three models were tested on experimental data collected at Monitoring Scan Point 3. It was observed from the prediction output that all the methods could provide applicable and practical results. However, using indicators like root mean square error (RMSE) and correlation coefficient (R) in assessing the output of the prediction, the C-LSTM had the best prediction output. This study contributes to the advancement of accurate and efficient prediction of surface displacement of underground mines, ultimately enhancing and assisting safety operations.展开更多
文摘Surface stability is essential in underground mines health management systems. Unexpected Surface displacement in underground mines could lead to loss of lives, injuries, and economic losses. To reduce or neutralise the adverse effects of surface displacement, it is vital to monitor and accurately predict them and unravel their mechanisms. In recent years, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) have proven effective in predicting complex problems. However, CNN neglects the dynamic dependency of the input in the temporal dimension, which affects surface displacement features. The Convolutional-LSTM model can dynamically learn the temporal dependency among input features via the feedback connections in the LSTM to improve accurate captures of surface displacement features. This study focused on evaluating the C-LSTM model in predicting surface displacement of underground mines and assessed the predictive capabilities and generalisation strength of using hybridised ANN models. Geodetic and geotechnical data gathered from a Gold Mine in Ghana was used. The three models were tested on experimental data collected at Monitoring Scan Point 3. It was observed from the prediction output that all the methods could provide applicable and practical results. However, using indicators like root mean square error (RMSE) and correlation coefficient (R) in assessing the output of the prediction, the C-LSTM had the best prediction output. This study contributes to the advancement of accurate and efficient prediction of surface displacement of underground mines, ultimately enhancing and assisting safety operations.
