Signal recognition and prediction system based on random forest model

To accurately predict the occurrence of rock bursts during deep coal mining and ensure the safe and efficient operation of coal mines, a signal recognition and prediction system based on the Random Forest model is proposed. This system utilizes electromagnetic radiation (EMR) and acoustic emission (AE) signal data, employing feature extraction and point biserial correlation analysis to screen out the most relevant feature parameters. A Random Forest binary classification model is constructed to identify interference signals. Subsequently, by introducing new features such as moving average slope and exponentially weighted moving average (EWMA), a time series analysis of precursor feature signals is conducted. A real-time warning model based on the Random Forest algorithm is developed, dynamically calculating the probability of precursor feature signal occurrence by integrating historical data and real-time data changes. This approach improves the accuracy and recall rate of signal recognition and prediction, providing reliable data support for mine safety management.