Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model
Abstract
:1. Introduction
2. Model Principles and Methods
2.1. PCA
- Standardize the sample data and calculate the correlation matrix.
- Solve the eigenvalues and sort the principal components according to their magnitudes.
- Calculate the variance contribution rate of each principal component and select m (m < n) principal components based on the principle of the cumulative variance contribution rate reaching 85%.
- Calculate the correlation coefficient matrix and list the calculation formula of the principal components so that the dimensionality reduction of the original data can be achieved.
2.2. PSO Method
2.3. ELM
2.4. Combined Early Warning Model of PCA–PSO–ELM
3. Example Analysis
3.1. Early Warning Indicator System for Rock Bursts
3.2. Principal Component Data Analysis
3.2.1. KMO Inspection and Bartlett Sphericity Test
3.2.2. Principal Component Extraction
3.3. Combined Early Warning Model of PCA–PSO–ELM for Rock Bursts
3.4. Results Analysis
4. Conclusions
- Taking the 10 factors that affected the occurrence of rock bursts in the Yanshitai Coal Mine as the main influencing factors, the PCA method was used to extract the dimension reduction feature information of the evaluation index, from which six principal components were extracted as the input vectors for extreme learning machine, eliminating the interaction between the various factors and simplifying the network structure of the model.
- Using the PSO algorithm, the initial weights and hidden layer neuron thresholds of the ELM were optimized. Therefore, a combined early warning PSO–ELM model for rock bursts was ultimately established, which overcame the disadvantage of randomness of the input weights and hidden layer thresholds of the ELM and enhanced the accuracy of the model prediction.
- Through the precision analysis and comparison between the prediction results of the BP neural network, radial basis function, and ELM, the research findings indicated that the prediction accuracy of the PSO–ELM model on the test set was as high as 100%, and the prediction accuracy was far better than the other three models.
- In summary, the rock burst PCA–PSO–ELM prediction model had the advantages for good prediction performance, fast learning speed, firm generalization ability, and satisfied robustness, which provided an idea for the early warning research of rock burst disasters and exhibited favorable engineering significance.
- It is worth noting that rock bursts are not only affected by the conditions of geology and mining technology, but also influenced by the hidden factors of Class II mining technology and so on during the prediction process of rock burst, such as the progress degree of the working face or the expansion and repair of roadway walls, prompting the risk rank assessment of rock bursts to become a difficult subject. Consequently, the investigation on intelligent early warning for rock bursts requires further research and discussion.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Assignment | Qualitative Index of Rock Bursts | |||||
---|---|---|---|---|---|---|
X4 | X5 | X6 | X8 | X9 | X10 | |
0 | Simple | Unchanged | Unchanged | No support or poor support | No pressure relief measures | None |
1 | Average | Minor change | Minor change | Average | Average pressure relief effect | Less |
2 | Relatively complex | Relatively large changes | Relatively large changes | Relatively good | Relatively good pressure relief effect | Many |
3 | Complex | Severe change | Severe change | Good | Good pressure relief effect |
Sample | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | Impact Level |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 1.3 | 29 | 530 | 0 | 0 | 0 | 0.07 | 3 | 3 | 0 | Ι |
2 | 1.2 | 25 | 542 | 0 | 0 | 0 | 0.24 | 3 | 3 | 0 | Ι |
3 | 1.4 | 44 | 560 | 0 | 0 | 0 | 0.09 | 3 | 3 | 0 | Ι |
4 | 3 | 24 | 573 | 0 | 0 | 1 | 0.36 | 2 | 3 | 0 | Ι |
5 | 0.8 | 34 | 553 | 1 | 0 | 0 | 0.15 | 0 | 2 | 1 | Ⅱ |
6 | 1.2 | 40 | 490 | 0 | 0 | 0 | 0.2 | 2 | 0 | 1 | Ⅱ |
7 | 1.4 | 35 | 480 | 0 | 0 | 1 | 0.36 | 2 | 0 | 1 | Ⅱ |
8 | 1.2 | 27 | 490 | 0 | 0 | 0 | 0.64 | 2 | 2 | 1 | Ⅱ |
9 | 2.6 | 48 | 752 | 2 | 0 | 2 | 0.48 | 1 | 1 | 1 | Ⅲ |
10 | 2.8 | 52 | 733 | 0 | 1 | 1 | 0.54 | 2 | 2 | 1 | Ⅲ |
11 | 3 | 78 | 560 | 1 | 3 | 2 | 1.14 | 2 | 3 | 1 | Ⅲ |
12 | 6 | 30 | 465 | 1 | 1 | 3 | 1.3 | 1 | 0 | 2 | Ⅲ |
13 | 1.5 | 65 | 570 | 1 | 3 | 1 | 0.28 | 1 | 2 | 2 | Ⅲ |
14 | 3 | 35 | 612 | 2 | 0 | 2 | 0.56 | 2 | 0 | 2 | Ⅲ |
15 | 2 | 35 | 614 | 1 | 0 | 2 | 0.56 | 1 | 0 | 2 | Ⅲ |
16 | 3 | 55 | 855 | 3 | 2 | 3 | 0.075 | 1 | 1 | 2 | Ⅳ |
17 | 4 | 52 | 675 | 3 | 2 | 3 | 1.88 | 0 | 0 | 2 | Ⅳ |
18 | 1.3 | 73 | 486 | 3 | 3 | 3 | 0.43 | 1 | 0 | 2 | Ⅳ |
19 | 2.1 | 67 | 498 | 3 | 3 | 3 | 1.89 | 0 | 0 | 2 | Ⅳ |
20 | 2.5 | 65 | 450 | 3 | 2 | 3 | 0.67 | 1 | 1 | 2 | Ⅳ |
21 | 1.7 | 60 | 314 | 3 | 3 | 1 | 1.3 | 0 | 0 | 2 | Ⅳ |
22 | 1.1 | 47 | 485 | 3 | 3 | 3 | 0.43 | 1 | 0 | 2 | Ⅳ |
23 | 1.8 | 54 | 238 | 3 | 1 | 3 | 1 | 0 | 0 | 2 | Ⅳ |
24 | 1.6 | 35 | 583 | 2 | 0 | 3 | 1.5 | 3 | 3 | 1 | Ⅱ |
25 | 1.5 | 35 | 530 | 0 | 0 | 0 | 0.56 | 3 | 3 | 0 | Ⅰ |
26 | 1.6 | 62 | 307 | 3 | 2 | 2 | 1 | 0 | 0 | 2 | Ⅳ |
27 | 1.9 | 59 | 542 | 1 | 2 | 3 | 0.25 | 0 | 0 | 1 | Ⅲ |
Sample | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | Impact Level |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 1.8 | 62 | 283 | 3 | 2 | 3 | 1 | 0 | 0 | 2 | Ⅳ |
2 | 1.3 | 44 | 570 | 0 | 0 | 0 | 0.66 | 3 | 3 | 0 | Ι |
3 | 2.2 | 54 | 290 | 3 | 2 | 2 | 1 | 0 | 0 | 2 | Ⅳ |
4 | 3 | 34 | 475 | 2 | 2 | 1 | 0.42 | 0 | 0 | 2 | Ⅲ |
5 | 3.2 | 42 | 574 | 3 | 0 | 0 | 0.29 | 0 | 0 | 2 | Ⅲ |
6 | 1.8 | 62 | 283 | 3 | 2 | 3 | 1 | 0 | 0 | 2 | Ⅳ |
7 | 1.3 | 44 | 656 | 2 | 1 | 3 | 0.24 | 1 | 1 | 2 | Ⅲ |
8 | 1.2 | 40 | 553 | 2 | 2 | 2 | 0.49 | 1 | 2 | 2 | Ⅲ |
Fitness Quantity of KMO Sampling | 0.753 | |
---|---|---|
Approximate chi square | 302.168 | |
Bartlett sphericity test | Degree of freedom | 45 |
Significance | 0.000 |
Sample | Principal Component | Impact Level | |||||
---|---|---|---|---|---|---|---|
Y1 | Y2 | Y3 | Y4 | Y5 | Y6 | ||
1 | −4.13 | −0.52 | 0.22 | 0.38 | 0.23 | 0.13 | Ⅰ |
2 | −4.15 | −0.38 | 0.17 | 0.55 | 0.38 | −0.09 | Ⅰ |
3 | −3.84 | −0.73 | 0.80 | 0.24 | 0.05 | 0.23 | Ⅰ |
4 | −3.22 | 0.99 | 0.37 | 0.73 | −0.23 | −0.2 | Ⅰ |
5 | −1.85 | −0.83 | −1.1 | −0.77 | −0.59 | −1.5 | Ⅱ |
6 | −2.12 | −0.56 | −1.35 | −0.17 | −0.30 | 0.7 | Ⅱ |
7 | −1.79 | −0.13 | −1.32 | 0.11 | 0.2 | 0.83 | Ⅱ |
8 | −2.67 | −0.29 | −0.79 | 0.71 | 0 | −0.35 | Ⅱ |
9 | −0.49 | 1.15 | 0.43 | −1.07 | 0.33 | −0.63 | Ⅲ |
10 | −1.63 | 0.74 | 1.42 | −0.3 | −0.68 | 0.17 | Ⅲ |
11 | 0.31 | −0.61 | 3.04 | 1.19 | −0.63 | 0.38 | Ⅲ |
12 | 1.39 | 3.2 | −0.65 | 1.65 | −0.81 | 0.99 | Ⅲ |
13 | 0.27 | −1.48 | 1.15 | −0.82 | −1.12 | 0.24 | Ⅲ |
14 | −0.06 | 1.57 | −0.78 | −0.53 | 0.64 | 0.56 | Ⅲ |
15 | −0.17 | 0.94 | −1.15 | −0.74 | 0.35 | 0.08 | Ⅲ |
16 | 1.17 | 1.16 | 1.43 | −2.5 | 0.17 | 0.03 | Ⅳ |
17 | 2.96 | 1.96 | 0.66 | 0.65 | −0.06 | −1.04 | Ⅳ |
18 | 2.34 | −1.54 | 0.55 | −0.89 | 0.44 | 0.86 | Ⅳ |
19 | 3.44 | −0.4 | 0.62 | 1.04 | 0.26 | −0.73 | Ⅳ |
20 | 1.96 | −0.5 | 0.51 | −0.05 | 0.36 | 0.47 | Ⅳ |
21 | 2.48 | −1.62 | −0.7 | 1.06 | −0.70 | −0.52 | Ⅳ |
22 | 1.80 | −1.02 | −0.29 | −0.84 | 0.80 | 0.55 | Ⅳ |
23 | 2.34 | −0.94 | −1.54 | 0.95 | 0.67 | 0.02 | Ⅳ |
24 | −1.14 | 0.75 | 1.15 | 1.42 | 2.49 | −0.62 | Ⅱ |
25 | −3.74 | −0.37 | 0.56 | 1.06 | 0.25 | −0.14 | Ⅰ |
26 | 2.39 | −1.44 | −0.92 | 0.62 | −0.05 | −0.18 | Ⅳ |
27 | 0.87 | −0.54 | 0.03 | −0.78 | −0.37 | 0.54 | Ⅲ |
Sample | Principal Component | Impact Level | |||||
---|---|---|---|---|---|---|---|
Y1 | Y2 | Y3 | Y4 | Y5 | Y6 | ||
1 | 2.74 | −1.24 | −0.83 | 0.73 | 0.34 | 0.16 | Ⅳ |
2 | −3.57 | −0.55 | 0.98 | 0.95 | 0.28 | −0.25 | Ⅰ |
3 | 2.33 | −0.94 | −1.16 | 0.83 | −0.22 | −0.1 | Ⅳ |
4 | 0.94 | 0.31 | −1.36 | −0.38 | −1.24 | −0.08 | Ⅲ |
5 | 0.42 | 0.81 | −1.59 | −1.1 | −1.11 | −0.9 | Ⅲ |
6 | 2.74 | −1.24 | −0.83 | 0.73 | 0.34 | 0.16 | Ⅳ |
7 | 0.37 | 0.09 | −0.03 | −1.56 | 0.96 | 0.01 | Ⅲ |
8 | 0.21 | −0.64 | 0.15 | −0.61 | 0.37 | −0.4 | Ⅲ |
Test Sample | Actual Grade | BP | RBF | ELM | PSO–ELM |
---|---|---|---|---|---|
1 | Ⅳ | Ⅳ | Ⅳ | Ⅳ | Ⅳ |
2 | Ⅰ | Ⅰ | Ⅰ | Ⅰ | Ⅰ |
3 | Ⅳ | Ⅳ | Ⅳ | Ⅳ | Ⅳ |
4 | Ⅲ | Ⅲ | Ⅲ | Ⅲ | Ⅲ |
5 | Ⅲ | Ⅳ | Ⅳ | Ⅲ | Ⅲ |
6 | Ⅳ | Ⅳ | Ⅳ | Ⅳ | Ⅳ |
7 | Ⅲ | Ⅲ | Ⅲ | Ⅳ | Ⅲ |
8 | Ⅲ | Ⅳ | Ⅱ | Ⅲ | Ⅲ |
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Yuan, H.; Ji, S.; Liu, G.; Xiong, L.; Li, H.; Cao, Z.; Xia, Z. Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model. Appl. Sci. 2023, 13, 8796. https://doi.org/10.3390/app13158796
Yuan H, Ji S, Liu G, Xiong L, Li H, Cao Z, Xia Z. Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model. Applied Sciences. 2023; 13(15):8796. https://doi.org/10.3390/app13158796
Chicago/Turabian StyleYuan, Haiping, Shuaijie Ji, Gaoliang Liu, Lijun Xiong, Hengzhe Li, Zhanhua Cao, and Zijin Xia. 2023. "Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model" Applied Sciences 13, no. 15: 8796. https://doi.org/10.3390/app13158796