# Research on a Coal Seam Gas Content Prediction Method Based on an Improved Extreme Learning Machine
## 1 Introduction
implemented the genetic algorithm (GA) and simulated annealing (SA) algorithms to optimize the BPNN for constructing a GASA-BPNN gas content prediction model. During the application process, the model showed a more robust generalization ability for new samples, faster parameter training speed, and higher prediction accuracy [15]. [PERSON] et al. introduced an adaptive learning rate to the BPNN and established the GASA-BPNN coal and gas outburst prediction model, which demonstrated good prediction performance during field applications [16]. [PERSON] et al. proposed a coal and gas outburst potential risk level prediction model that combines fault tree and ANN coupling. By utilizing qualitative and quantitative data to solve the model, they made the relationship between geological factors and gas outburst potential risk more evident [17]. [PERSON] et al. utilized an improved fruit fly optimization algorithm (IFOA) to optimize the general regression neural network (GRNN), leading to the development of the IFOA-GRNN model for predicting coal and gas outbursts. The IFOA-GRNN model displays the desirable attributes of low prediction errors, high stability, and rapid convergence speed during practical applications [18]. [PERSON] et al. proposed a coal seam gas content prediction model based on an SVM and PSO. The results of practical applications revealed that the PSO-SVR model outperforms both the ANN model and the ordinary support vector regression (SVR) model, particularly with a limited number of samples [19]. [PERSON] et al. improved the grey prediction model by incorporating a BPNN, ultimately establishing an enhanced gas concentration prediction model based on grey theory and the BPNN. The simulation results indicate that this model significantly improves the prediction accuracy of the gas concentration [20]. [PERSON] et al. introduced an improved residual gas content prediction method based on the drilling cutting index and the bat algorithm-optimized ELM. In comparison with the BPNN, SVM, and ELM, this novel method exhibits superior accuracy and effectively uncovers the nonlinear relationship between the drilling cutting index and residual gas content [21]. [PERSON] et al. established a protrusion risk prediction model based on a convolutional neural network. This model explores the correlation between post-explosion gas concentration changes and coal seam protrusion risks, which is crucial in improving coal and gas outburst prediction accuracy [22]. [PERSON] et al. proposed a gas outburst prediction model based on the grey relation analysis (GRA) and adaptive PSO algorithm-optimized SVM. Their study demonstrates that the new model exhibits better performance than the SVM and PSO-SVM outburst prediction models [23]. With regard to predicting coalbed methane content, the predominant approach involves leveraging gas geological theory to analyze the factors that influence methane content. By comprehensively considering these factors and employing mathematical techniques to establish a functional mapping relationship between the influencing factors and the target of prediction, accurate predictions can be achieved. The results demonstrate that this method surpasses traditional prediction methods and exhibits a relatively high accuracy rate. To overcome the limitations of ELM, BPNN, and SVM, researchers have integrated intelligent algorithms to optimize their parameters. However, the application of algorithms for parameter optimization is based on a random search algorithm framework, which still has room for improvement in terms of functionality. During the process of developing prediction models, researchers commonly rely on a single algorithm to optimize model parameters. Nevertheless, using a single algorithm may lead to limitations such as being vulnerable to local optima, exhibiting poor generalization ability, and achieving low accuracy when addressing intricate issues.