A data-driven method approach for prediction of coal seam gas content combining feature selection and machine learning

Coal seam gas content (CSGC) is a key factor for mine safety and methane resource utilization. To address these limitations, an integrated feature selection and machine learning approach was developed for improved gas content prediction. In this study, a total of 27 sets of experimental data covering geologic structure, coal quality characteristics, and coal occurrence conditions were collected from the left flank area of Taoyuan Coal Mine in Huaibei, China. Missing values in the dataset were handled using the chain interpolation method. Subsequently, the Genetic Algorithm with Ant Colony Optimization (GA-ACO) was introduced and compared with several traditional feature selection algorithms, including All Subset Regression (ASR), Forward Selection Regression (FSR), Backward Selection Regression (BSR), Random Forest Algorithm (RFA), and Lasso regression to obtain the main controlling factors of gas content. The Particle Swarm Optimization Support Vector Regression (PSO-SVR) to build a regression prediction model. Various regression models such as Multivariate Linear Regression (MLR), Multi-Layer Perceptron (MLP), Gradient Boosting Regression Tree (GBRT) and SVR were then used to construct regression prediction models. The dataset was randomly divided into training and validation sets in an 8:2 ratio, and prediction performance was evaluated using metrices. The results showed that the use of feature selection and machine learning effectively predicted the gas content of coal seams. The PSO-SVR model based on ASR achieved the best prediction performance with an R 2 of 0.99 on the training set and 0.978 on the test set, whit a maximum error of 0.077. Finally, the constructed model was used to predict regional gas content and a regional gas content distribution map was plotted, providing a visual representation for analyzing gas content distribution in coal seams. This study demonstrates the potential of combining feature selection with machine learning for regression prediction in coal bed geology, offering a feasible and promising approach for research in this field. • An innovative data-driven approach is established to predict gas content. • Construct controlling factors set based on multiple feature selection algorithms. • Prediction model of GA-ACO combined with PSO-SVR are proposed. • Gas content distribution map is drawn using the best prediction model.