# Research on a Coal Seam Gas Content Prediction Method Based on an Improved Extreme Learning Machine
## 1 Introduction  
Coal resources play a crucial role as an energy source in China and have contributed immensely to the country's economic development [1, 2], and given China's current energy structure, coal is expected to maintain its dominant position in the energy supply for the foreseeable future [3]. Based on statistics from the National Bureau of Statistics, China is endowed with abundant coal resources, with proven coal reserves of 249.23 billion tons. Additionally, China holds the position of the world's largest producer and consumer of coal. In 2021, the total primary energy production in China was recorded at 4.33 billion tons of standard coal, with coal accounting for 67% of the overall energy structure. The total energy consumption was 5.24 billion tons of standard coal, with coal consumption contributing to 56.0% of the total primary energy consumption. Ensuring the healthy, stable, and sustainable development of the coal industry is crucial to maintaining the country's energy security [4]. However, significant disparities in coal resource endowments, complex geological conditions, and deep burial depths across different regions pose substantial obstacles to achieving this goal. China's robust demand for coal has resulted in an increase in coal mining depths, and mining operations now face high gas pressure, elevated groundstress, and heightened gas content, posing significant threats to mine safety. Although the number of gas accidents has decreased significantly in recent years, the number of fatalities remains high compared to other nations. The issue of mine gas is a major problem that restricts the capacity of coal production, affects the safety of workers, and impedes the economic benefits of coal mines [5; 6]. The unclear distribution pattern and occurrence status of CBM, as well as the failure to implement effective gas prevention and control measures, are the primary causes of most gas accidents. Coal seam gas extraction is not only a source of clean and natural energy but also a means to address the pressing issue of gas control [7]. Undoubtedly, methane, a crucial element of coal mine gas, is a potent greenhouse gas [8]. Its ozone-depleting capacity is sevenfold higher than that of carbon dioxide, and its heat-trapping potential is 25 to 30 times higher than an equivalent volume of carbon dioxide. Hence, it holds immense importance to investigate techniques for predicting coalbed methane content from multiple standpoints, such as mitigating gas calamities and safeguarding human lives and property, exploiting and harnessing clean energy, and conserving the environment. In recent years, with the rapid advancement of machine learning and intelligent algorithms, novel methods such as artificial neural networks (ANNs), support vector machines (SVMs), and extreme learning machines (ELMs) have presented new avenues for tackling high-dimensional, nonlinear, and complex function optimization problems [9]. [PERSON] et al. utilized the SVM to construct a model for predicting the dissolved gas content in power transformers [10]. [PERSON] et al. developed a short-term natural gas load forecasting model using a back propagation neural network (BPNN) [11]. [PERSON] et al. employed a BPNN to identify the deformation characteristics of ACM oil and gas pipelines [12]. [PERSON] et al. used the ANN to establish a shallow gas identification model for deep water drilling [13]. Machine learning algorithms have garnered widespread application and have yielded fruitful research outcomes in coal seam gas content prediction, coal and gas outburst prediction, and gas outflow prediction. [PERSON] et al. utilized particle swarm optimization (PSO) to optimize the initial weights and thresholds of the BPNN to create a PSO-BPNN gas content prediction model. In comparison with the multiple linear regression model and the BPNN model, the PSO-BPNN model demonstrated the highest prediction accuracy [14]. [PERSON] et al.