An approach to predict the height of fractured water-conducting zone of coal roof strata using random forest regression

Abstract

Water inrushes from coal-roof strata account for a great proportion of coal mine accidents, and the height of fractured water-conducting zone (FWCZ) is of significant importance for the safe production of coal mines. A novel and promising model for predicting the height of FWCZ was proposed based on random forest regression (RFR), which is a powerful intelligent machine learning algorithm. RFR has high prediction accuracy and is robust in dealing with the complicated and non-linear problems. Also, it can evaluate the importance of the variables. In this study, the proposed model was applied to Hongliu Coal Mine in Northwest China. 85 field measured samples were collected in total, with 60 samples (70%) used for training and 20 (30%) used for validation. For comparison, a support vector machine (SVM) model was also constructed for the prediction. The results show that the two models are in accordance with the field measured data, and RFR shows a better performance on good tolerance to outliers and noises and efficiently on high-dimensional data sets. It is demonstrated that RFR is more practicable and accurate to predict the height of FWCZ. The achievements will be helpful in preventing and controlling the water inrushes from coal-roof strata, and also can be extended to various engineering applications.