A Gas Emission Prediction Model Based on Feature Selection and Improved Machine Learning
Abstract
:1. Introduction
2. Data Processing for Gas Emission Prediction
2.1. Initial Index System of Gas Emission Prediction
2.2. Data Standardization Processing
3. Construction of the Mine Gas Emission Prediction Model
3.1. Determination of Characteristic Parameter Sets for Gas Emission Prediction
3.1.1. Total Subset Regression
3.1.2. Kernel Principal Component Analysis (KPCA)
3.2. A Selection of Gas Emission Prediction Algorithms
4. Optimal Fusion Model Selection
4.1. Determination of the Optimal Parameter Set
4.2. Determination of the Optimal Improved Machine Learning Algorithm
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | X11 | X12 | Y | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 4.55 | 3.78 | 9.84 | 550.32 | 4.20 | 15.83 | 3.02 | 913.54 | 7.40 | 0.35 | 4.80 | 1 | 25.73 |
2 | 3.95 | 3.53 | 8.78 | 577.5 | 3.60 | 20.34 | 3.38 | 920.65 | 7.20 | 0.32 | 4.67 | 1 | 23.44 |
3 | 2.85 | 2.2 | 6.13 | 510.58 | 2.40 | 24.37 | 2.91 | 793.20 | 6.70 | 0.25 | 4.50 | 1 | 18.30 |
4 | 3.81 | 2.93 | 8.51 | 639.53 | 3.70 | 25.22 | 3.48 | 854.84 | 7.30 | 0.27 | 3.72 | 1 | 21.07 |
5 | 4.22 | 3.67 | 7.54 | 650.12 | 3.90 | 26.21 | 3.31 | 872.09 | 7.10 | 0.23 | 4.04 | 1 | 23.30 |
6 | 4.13 | 3.59 | 8.69 | 641.82 | 3.20 | 33.28 | 2.43 | 875.15 | 6.50 | 0.33 | 3.35 | 1 | 22.17 |
7 | 4.34 | 3.72 | 8.74 | 664.48 | 3.90 | 22.06 | 2.90 | 865.18 | 7.20 | 0.35 | 3.11 | 1 | 23.63 |
8 | 4.57 | 3.82 | 10.57 | 720.22 | 4.40 | 17.21 | 3.11 | 925.24 | 7.50 | 0.36 | 3.79 | 1 | 26.12 |
9 | 3.81 | 3.58 | 7.38 | 652.35 | 3.30 | 15.73 | 3.69 | 840.59 | 7.30 | 0.28 | 3.22 | 1 | 22.61 |
10 | 2.89 | 2.35 | 5.96 | 491.75 | 2.50 | 26.87 | 3.56 | 812.59 | 7.40 | 0.29 | 3.01 | 1 | 16.63 |
11 | 3.14 | 3.23 | 6.38 | 508.17 | 2.90 | 29.10 | 2.81 | 834.33 | 6.90 | 0.23 | 3.19 | 1 | 18.25 |
12 | 4.57 | 3.74 | 8.85 | 712.25 | 3.90 | 17.56 | 3.40 | 846.53 | 7.40 | 0.33 | 2.49 | 1 | 24.60 |
13 | 3.51 | 2.76 | 7.26 | 531.35 | 3.20 | 27.76 | 2.85 | 867.83 | 6.80 | 0.28 | 2.50 | 1 | 19.00 |
14 | 3.71 | 2.84 | 9.8 | 629.55 | 3.40 | 13.30 | 3.04 | 913.71 | 7.30 | 0.30 | 3.48 | 1 | 22.67 |
15 | 3.76 | 3.37 | 9.37 | 639.67 | 3.50 | 16.58 | 3.05 | 885.61 | 7.20 | 0.26 | 3.17 | 1 | 23.05 |
16 | 3.15 | 2.51 | 6.36 | 514.03 | 2.80 | 18.90 | 2.50 | 859.43 | 6.80 | 0.22 | 2.89 | 1 | 19.34 |
17 | 4.11 | 3.54 | 7.78 | 597.87 | 3.70 | 11.35 | 3.45 | 871.07 | 6.90 | 0.33 | 2.50 | 1 | 20.93 |
18 | 4.18 | 2.75 | 7.08 | 502.45 | 3.60 | 32.53 | 3.57 | 904.41 | 6.90 | 0.18 | 3.00 | 1 | 18.54 |
19 | 2.71 | 2.81 | 6.45 | 488.96 | 2.70 | 26.46 | 3.46 | 847.72 | 6.80 | 0.15 | 3.30 | 1 | 19.65 |
20 | 3.64 | 2.89 | 6.85 | 465.42 | 3.40 | 28.99 | 2.46 | 816.14 | 6.60 | 0.18 | 3.36 | 1 | 16.65 |
21 | 3.66 | 3.40 | 8.35 | 516.57 | 3.40 | 23.72 | 2.75 | 840.67 | 7.40 | 0.17 | 3.70 | 1 | 18.52 |
22 | 4.07 | 3.09 | 5.48 | 572.34 | 2.80 | 26.16 | 3.33 | 874.15 | 7.00 | 0.20 | 3.64 | 1 | 19.73 |
23 | 3.74 | 3.30 | 8.22 | 623.52 | 3.20 | 13.36 | 2.97 | 847.57 | 6.60 | 0.18 | 3.51 | 1 | 20.85 |
24 | 2.73 | 2.57 | 5.86 | 457.53 | 2.50 | 24.69 | 3.10 | 778.53 | 7.30 | 0.21 | 2.98 | 1 | 15.67 |
25 | 3.42 | 2.30 | 6.13 | 493.20 | 2.20 | 28.17 | 2.91 | 785.41 | 6.60 | 0.20 | 3.29 | 1 | 17.24 |
26 | 3.65 | 3.94 | 6.52 | 584.00 | 2.80 | 23.93 | 2.92 | 793.10 | 6.60 | 0.22 | 4.08 | 1 | 19.76 |
27 | 3.15 | 2.96 | 5.26 | 536.24 | 2.90 | 33.45 | 2.66 | 811.35 | 6.50 | 0.18 | 3.27 | 1 | 18.65 |
28 | 4.13 | 3.32 | 7.18 | 648.45 | 3.90 | 24.19 | 3.47 | 894.11 | 7.30 | 0.34 | 4.52 | 1 | 21.83 |
29 | 4.24 | 3.62 | 8.67 | 671.30 | 3.80 | 17.52 | 2.56 | 861.71 | 7.30 | 0.27 | 4.48 | 1 | 22.15 |
30 | 2.46 | 2.82 | 5.83 | 505.57 | 2.40 | 33.57 | 2.79 | 779.70 | 6.30 | 0.14 | 3.73 | 1 | 14.57 |
Influencing Factor | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | X11 | X12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
F-0 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ |
F-1 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | |||||
F-2 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | |||||
F-3 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | |||||
F-4 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | |||||
F-5 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | |||||
F-6 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ||||||
F-7 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ||||||
F-8 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ||||||
F-9 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ||||||
F-10 | ☆ | ☆ | ☆ | ☆ | ☆ | |||||||
F-11 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ||||||
F-12 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ||||||
F-13 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ||||||
F-14 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ||||||
F-15 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ||||||
F-16 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | |||||
F-17 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ | |||||
F-18 | ☆ | ☆ | ☆ | ☆ | ☆ | ☆ |
Kernel Principal Component | F1 | F2 | F3 | F4 | F5 | F6 | F7 | F8 | F9 | F10 | F11 | F12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
variance contribution rate % | 53.11 | 11.41 | 8.31 | 6.57 | 5.63 | 4.30 | 3.38 | 2.14 | 1.75 | 1.35 | 0.90 | 0.52 |
Parameter Combinations | Improved Algorithm | RMSE | MAE | MAPE | R2 | NSE |
---|---|---|---|---|---|---|
F-0 | GA-HKELM | 0.93431 | 0.63198 | 3.8216% | 0.88091 | 0.87333 |
F-K | SSA-HKELM | 1.02890 | 0.71577 | 4.2977% | 0.88348 | 0.84639 |
F-4 | WOA-HKELM | 0.28456 | 0.25234 | 1.3347% | 0.98987 | 0.98825 |
F-5 | SSA-HKELM | 0.37306 | 0.26626 | 1.3719% | 0.99184 | 0.97980 |
F-5 | SMA-HKELM | 0.25932 | 0.23025 | 1.2184% | 0.99194 | 0.99024 |
F-5 | WOA-HKELM | 0.22865 | 0.20306 | 1.0595% | 0.99395 | 0.99241 |
F-11 | SSA-HKELM | 0.37306 | 0.26626 | 1.3719% | 0.99184 | 0.97980 |
F-11 | MFO-HKELM | 0.31620 | 0.24417 | 1.2260% | 0.99592 | 0.98549 |
F-11 | WOA-HKELM | 0.31637 | 0.24134 | 1.2068% | 0.99594 | 0.98548 |
F-17 | MFO-HKELM | 0.31620 | 0.24417 | 1.2260% | 0.99592 | 0.98549 |
F-17 | WOA-HKELM | 0.31637 | 0.24134 | 1.2068% | 0.99594 | 0.98548 |
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Shao, L.; Zhang, K. A Gas Emission Prediction Model Based on Feature Selection and Improved Machine Learning. Processes 2023, 11, 883. https://doi.org/10.3390/pr11030883
Shao L, Zhang K. A Gas Emission Prediction Model Based on Feature Selection and Improved Machine Learning. Processes. 2023; 11(3):883. https://doi.org/10.3390/pr11030883
Chicago/Turabian StyleShao, Liangshan, and Kun Zhang. 2023. "A Gas Emission Prediction Model Based on Feature Selection and Improved Machine Learning" Processes 11, no. 3: 883. https://doi.org/10.3390/pr11030883