# Research on a Coal Seam Gas Content Prediction Method Based on an Improved Extreme Learning Machine
## 2 Development of Prediction Models for Coal Seam Gas Content
### Development of a Gas Content Prediction Model Based on the GASA-KELM Algorithm  
Multilayer feedforward neural networks (FFNNs) have been widely used in linear and nonlinear system identification due to their excellent global approximation performance [31]. However, most traditional FFNN learning algorithms are based on the gradient descent method to modify all the weights and thresholds of the entire network. This has the disadvantages of requiring a large number of iterations, which makes it difficult to select an appropriate learning rate. To overcome the shortcomings of traditional FFNN algorithms, [PERSON] proposed a new algorithm called ELM for solving single-hidden-layer feedforward neural networks [32]. Compared with traditional algorithms, ELM randomly initializes the connection weights and thresholds of FFNN and does not participate in learning and correction. The unique optimal solution can be obtained by adjusting the number of hidden layer nodes.  
The proposed model for predicting gas content in coal seams based on the improved ELM introduces the radial basis function kernel due to the nonlinear nature of the gas content prediction system. The initial kernel parameters, which include the bandwidth of the Gaussian kernel and regularization factor of KELM, have a significant impact on the model's predictive performance. The issue of randomly assigned kernel parameters leading to a non-full rank  
==WARNING: Truncated because of repetitions==
. The training sample data for the model are shown in Table 3.  
\begin{table} \
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sample Number** & \(\mathbf{X_{9}}\)**/**(m\({}^{3}\).t\({}^{-1}\))** & \(\mathbf{X_{1}}\)**/**(m)** & \(\mathbf{X_{2}}\)**/**(m)** & \(\mathbf{X_{3}}\) & \(\mathbf{X_{4}}\)**/**(m)** & \(\mathbf{X_{5}}\) & \(\mathbf{X_{6}}\) & \(\mathbf{X_{7}}\) & \(\mathbf{X_{8}}\)**/(m)** \\ \hline
1 & 17.01 & 407.12 & 6.44 & 0.0198 & 254.09 & 0.4185 & 0.0008 & 0.0005 & \(-324.81\) \\
2 & 25.32 & 350.41 & 6.08 & 0.0191 & 209.54 & 0.9000 & 0.1416 & 0.1812 & \(-265.47\) \\
3 & 25.63 & 327.86 & 5.74 & 0.0354 & 191.74 & 0.4508 & 0.3062 & 0.0001 & \(-243.64\) \\
4 & 15.27 & 438.26 & 4.84 & 0.0213 & 241.67 & 0.4000 & 0.0016 & 0.0001 & \(-353.7\) \\
5 & 18.99 & 363.62 & 5.79 & 0.0440 & 219.25 & 0.5663 & 0.1003 & 0.0004 & \(-279.48\) \\
6 & 12.53 & 400.92 & 3.61 & 0.0193 & 256.23 & 0.7740 & 0.0264 & 0.0048 & \(-315.06\) \\
7 & 22.03 & 524.6 & 5.35 & 0.0122 & 267.32 & 0.5109 & 0.3255 & 0.0025 & \(-437.3\) \\
8 & 21.46 & 517.3 & 4.36 & 0.0171 & 114.52 & 0.3855 & 0.0853 & 0.0115 & \(-430\) \\
9 & 24.46 & 437.4 & 4.81 & 0.0224 & 121.89 & 0.2370 & 0.1189 & 0.0137 & \(-350.7\) \\
10 & 24.03 & 444 & 7.07 & 0.0165 & 188.67 & 0.6074 & 0.0352 & 0.0084 & \(-359.82\) \\
11 & 31.01 & 305.42 & 5.39 & 0.0403 & 207.99 & 0.3394 & 0.1738 & 0.0002 & \(-217.27\) \\
12 & 19.17 & 326.7 & 5.43 & 0.0546 & 241.06 & 0.5873 & 0.0032 & 0.0117 & \(-242.16\) \\
13 & 27.63 & 530 & 10.59 & 0.0412 & 334.41 & 0.4105 & 0.0421 & 0.0004 & \(-440.9\) \\
14 & 20.41 & 501.7 & 5.34 & 0.0159 & 128.22 & 0.3534 & 0.0266 & 0.0010 \end{tabular}\end{table}
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