Predicting the Risk of Fault-Induced Water Inrush Using the Adaptive Neuro-Fuzzy Inference System
Abstract
:1. Introduction
2. Brief Description of Fault-Induced Water Inrush
2.1. Water-Conducting Property of Fault
2.2. Brief Introduction to Fault-Induced Water Inrush
3. Methodology of the ANFIS
3.1. Architecture of ANFIS
3.2. Hybrid Learning Rule of the ANFIS
4. Prediction of Fault-Induced Water Inrush with the ANFIS
4.1. ANFIS Training
4.2. Results and Remarks
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Mines | (WP)/MPa | (MH)/m | (AT)/m | (FT)/m | (DACS)/° | (DWF)/m | Whether Water Inrush Accident Occurred in Actual Situation? |
---|---|---|---|---|---|---|---|
Xiazhuang coal mine case 1 | 1.82 | 0.8 | 26.39 | 4 | 12 | 16 | Yes |
Xiazhuang coal mine case 2 | 1.65 | 1.6 | 25.85 | 50 | 17 | 90 | Yes |
Xiazhuang coal mine case 3 | 1 | 0.9 | 22.33 | 2 | 13 | 16 | Yes |
Xiazhuang coal mine case 4 | 2.88 | 1 | 17.68 | 1.3 | 20 | 0 | Yes |
Jingxing coal mine case 1 | 2.01 | 8 | 28 | 0.6 | 18 | 10 | Yes |
Jingxing coal mine case 2 | 1.91 | 8 | 43 | 1.5 | 11 | 2 | Yes |
Hongshan coal mine case 1 | 1.33 | 0.85 | 36.38 | 0.8 | 7 | 62 | No |
Hongshan coal mine case 2 | 0.95 | 1.45 | 26.89 | 1 | 6 | 55 | No |
Hongshan coal mine case 3 | 0.92 | 1.4 | 33.61 | 0.5 | 8 | 0 | No |
Hongshan coal mine case 4 | 0.34 | 0.9 | 32.65 | 22 | 6 | 6 | No |
Heishan coal mine case 1 | 1.06 | 2 | 27.79 | 0.46 | 7 | 21 | No |
Heishan coal mine case 2 | 0.83 | 2.85 | 26.56 | 0.7 | 12 | 6 | No |
Xieyi coal mine | 2 | 2.81 | 30 | 1.5 | 18 | 12 | Yes |
Jiulishan coal mine | 1.87 | 1.9 | 23 | 0.5 | 15 | 17 | Yes |
Pandong coal mine | 1.7 | 2.8 | 10 | 5 | 17 | 10 | Yes |
Taoyang coal mine | 0.6 | 1.1 | 17 | 8 | 19 | 6 | Yes |
Huatai coal mine | 2.1 | 1.6 | 59.5 | 3.5 | 10 | 39 | No |
Panxi coal mine case 1 | 2.8 | 2.75 | 69.17 | 11.7 | 12 | 36 | No |
Panxi coal mine case 2 | 2.8 | 2.55 | 66.11 | 16 | 12 | 29 | No |
Xiezhuang coal mine | 1.3 | 1.7 | 30 | 4.9 | 5 | 21 | Yes |
Working Faces | (WP)/MPa | (MH)/m | (AT)/m | (FT)/m | (DACS)/° | (DWF)/m |
---|---|---|---|---|---|---|
31,503 working face in Huatai coal mine | 1.08 | 0.90 | 16.50 | 3.2 | 7 | 7 |
51,302 working face in Liangzhuang coal mine | 1.10 | 1.60 | 20.00 | 15.0 | 11 | 16 |
6194 working face in Panxi coal mine | 4.06 | 2.75 | 65.86 | 10.0 | 10 | 11 |
9602 working face in Baizhuang coal mine | 3.11 | 2.61 | 44.30 | 3.5 | 11 | 12 |
61,106 working face in Huahen coal mine | 2.70 | 2.55 | 66.97 | 16.0 | 12 | 31 |
Working Faces | Water Inrush Index Obtained by FIS Reasoning | Probability of Water Inrush Occurring | Probability of Water Inrush Not Occurring | Whether Water Inrush Occurred in Actual Situation | Whether the Prediction Is Consistent with the Actual Situation |
---|---|---|---|---|---|
31,503 working face in Huatai coal mine | 0.661 | 0.8305 | 0.1695 | Yes | Yes |
51,302 working face in Liangzhuang coal mine | 0.658 | 0.829 | 0.171 | Yes | Yes |
6194 working face in Panxi coal mine | −0.999 | 0.0005 | 0.9995 | No | Yes |
9602 working face in Baizhuang coal mine | 0.122 | 0.561 | 0.439 | Yes | Yes |
61,106 working face in Huahen coal mine | −1 | 0 | 1 | No | Yes |
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Zhou, Q.; Herrera-Herbert, J.; Hidalgo, A. Predicting the Risk of Fault-Induced Water Inrush Using the Adaptive Neuro-Fuzzy Inference System. Minerals 2017, 7, 55. https://doi.org/10.3390/min7040055
Zhou Q, Herrera-Herbert J, Hidalgo A. Predicting the Risk of Fault-Induced Water Inrush Using the Adaptive Neuro-Fuzzy Inference System. Minerals. 2017; 7(4):55. https://doi.org/10.3390/min7040055
Chicago/Turabian StyleZhou, Qinglong, Juan Herrera-Herbert, and Arturo Hidalgo. 2017. "Predicting the Risk of Fault-Induced Water Inrush Using the Adaptive Neuro-Fuzzy Inference System" Minerals 7, no. 4: 55. https://doi.org/10.3390/min7040055