Machine Learning in predicting the extent of gas and rock outburst

Abstract

In order to develop a method for forecasting the costs generated by rock and gas outbursts for hard coal deposit "Nowa Ruda Pole Piast Wacław-Lech", the analyses presented in this paper focused on key factors influencing the discussed phenomenon. Part of this research consisted in developing a prediction model of the extentof rock and gas outbursts with regard to the most probable mass of rock [Mg] and volume of gas [m3] released in an outburst and to the length of collapsed and/or damaged workings [running meters, rm]. For this purpose, a machine learning method was used, i.e. a "random forests method" with the "XGBoost" machine learning algorithm. After performing the machine learning process with the cross-validation technique, with five iterations, the lowest possible values of the mean-square prediction error "RMSE" were achieved. The obtained model and the program written in the programming language "R" was verified on the basis of the "RMSE" values, prediction matching graphs, out of sample analysis, importance ranking of input parameters and the sensitivity of the model during the forecast for hypothetical conditions.

Numerical modelling of gas outburst from coal: a review from control parameters to the initiation process

“…An attempt towards using regression methods was conducted by Bodlak et al (2018) who used the random forest method, which is an ensemble technique that combines multiple decision trees to create a more robust prediction model. They employed the XGBoost algorithm, a variant of gradient boosting that is designed to handle large datasets and deliver high accuracy in a computationally efficient manner.…”