A Prediction Method for Height of Water Flowing Fractured Zone Based on Sparrow Search Algorithm–Elman Neural Network in Northwest Mining Area
Abstract
:1. Introduction
2. Geological Setting
3. Influencing Factors and Data Preprocessing
3.1. Selection of Influencing Factors
- 1.
- Mining method (Im);
- 2.
- The ratio of the thickness of hard rock layers and soft rock layers in the bedrock (Ir);
- 3.
- Buried depth (Id);
- 4.
- Mining height (Ih);
- 5.
- Working face length (Il).
3.2. Data Preprocessing
3.3. Correlations among Input Features and Output
4. SSA-ELMAN Neural Network Model
4.1. Elman Neural Network
4.2. Sparrow Search Algorithm
- 1.
- Producers;
- 2.
- Scroungers;
- 3.
- Guards;
4.3. Construction of Prediction Model Based on SSA-Elman Neural Network
5. Verification of the Prediction Model for Height of WFFZ
5.1. Engineering Background
5.2. Field Measurements of Height of WFFZ
5.3. Model Verification
5.4. Feature Importance
6. Conclusions
- (1)
- The Jurassic coal seams in the Inner Mongolia-Shaanxi border area are middle-deep buried underground and have special overburden strata. We considered five factors (mining method, the ratio of the thickness of hard rock layers and soft rock layers in the bedrock, buried depth, mining height, and working face length) that influence the development of the height of the water-flowing fractured zone. In particular, the ratio of the thickness of the hard rock layers to the thickness of the soft rock layers in the bedrock was selected as a parameter to characterize an overburdened structure. The linear correlation between coal seam mining conditions and parameters and the height of WFFZ is analyzed by the Pearson correlation coefficient, which offers a foundation for developing a prediction model.
- (2)
- An SSA-Elman neural network water-flowing fractured zone height prediction model was established using the sparrow search algorithm to optimize the weights and thresholds of the original Elman neural network. This model can fit the nonlinear relationship between the development of the WFFZ and its influencing factors better in the Inner Mongolia-Shaanxi border area.
- (3)
- In response to the requirement for accurate water-transmission fissure heights in the middle and deep Jurassic coal seams, we used the MAE and RMSE to quantitatively evaluate the performances of the different models. The results show that the MAE and RMSE of the SSA-Elman neural network model in the evaluation stage are 3.93 and 4.93, respectively, which indicates that this model has the best prediction accuracy compared with traditional calculation methods and prediction models.
- (4)
- Using the mining conditions of panel 31110X of Coal Mine A as an engineering example, we obtained a prediction value of 124.9 m using the SSA-Elman neural network model. This has the smallest error of the prediction models compared with the measured value of 126 m, demonstrating that the SSA-Elman neural network model developed in the study is more accurate in predicting the height of the water flowing fractured zone and more closely represents the real situation. Thus, it can provide guidance regarding the water inflow from a mining face and the use of advanced water discharge techniques in the Inner Mongolia-Shaanxi border area.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Panel | Measured Value | Normalized Value | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Im | Ir | Ih (m) | Id (m) | Il (m) | Ia (m) | Im | Ir | Ih | Id | Il | Ia | |
Hulusu 21102 | ZC 1 | 1.53 | 2.85 | 634.8 | 320 | 45.6 | 0 | 0.04 | 0.01 | 0.87 | 0.85 | 0.00 |
Hongqinghe 31101 | ZC | 2.82 | 6.5 | 669 | 240 | 106.1 | 0 | 0.65 | 0.51 | 0.95 | 0.56 | 0.30 |
Nalinhe mine #2 | ZC | 2.93 | 5.5 | 580 | 247 | 103.23 | 0 | 0.70 | 0.37 | 0.75 | 0.58 | 0.29 |
Hanglaiwan 30101 | ZC | 3.13 | 4.8 | 248 | 300 | 122.02 | 0 | 0.80 | 0.28 | 0.00 | 0.78 | 0.38 |
Tingnan mine 107 | ZF 2 | 1.45 | 9.9 | 650 | 116 | 165.83 | 1 | 0.00 | 0.97 | 0.91 | 0.10 | 0.60 |
Xiagou mine 2801 | ZF | 1.45 | 9.9 | 332 | 90 | 149 | 1 | 0.00 | 0.97 | 0.19 | 0.00 | 0.52 |
Caojiatan 122106 | ZC | 3.13 | 6.0 | 280 | 360 | 136.10 | 0 | 0.80 | 0.44 | 0.07 | 1.00 | 0.45 |
Huoshizui mine 8712 | ZF | 1.45 | 10 | 628.16 | 200 | 220 | 1 | 0.00 | 0.99 | 0.86 | 0.41 | 0.87 |
Dafosi 40106 | ZF | 1.45 | 9.1 | 450 | 180 | 245.51 | 1 | 0.00 | 0.86 | 0.45 | 0.33 | 1.00 |
Hujiahe 401101 | ZF | 1.45 | 10.1 | 644.52 | 180 | 225.43 | 1 | 0.00 | 1.00 | 0.89 | 0.33 | 0.90 |
Yushuwan 20104 | ZC | 3.13 | 5 | 280 | 255 | 138.40 | 0 | 0.80 | 0.31 | 0.07 | 0.61 | 0.46 |
Jinjitan 12−2, upper 101 | ZC | 3.13 | 5.0 | 260 | 300 | 120.25 | 0 | 0.80 | 0.31 | 0.03 | 0.78 | 0.37 |
Lingxin mine L331 | ZC | 3.56 | 2.75 | 250 | 280 | 57.35 | 0 | 1.00 | 0.00 | 0.00 | 0.70 | 0.06 |
Hongliu mine 1121 | ZC | 3.56 | 5.3 | 330 | 302 | 62.5 | 0 | 1.00 | 0.35 | 0.18 | 0.79 | 0.08 |
Menkeqing 11-3102 | ZC | 1.53 | 4.75 | 692 | 300 | 117 | 0 | 0.04 | 0.27 | 1.00 | 0.78 | 0.36 |
Muduchaideng mine, coal seam 3-1 | ZC | 1.53 | 4.8 | 685 | 250 | 106 | 0 | 0.04 | 0.28 | 0.98 | 0.59 | 0.30 |
Jinfeng mine 011802 | ZC | 3.56 | 4.6 | 500 | 280 | 63.12 | 0 | 1.00 | 0.25 | 0.57 | 0.70 | 0.09 |
Shilawusu mine 221, upper 06A | ZF | 1.53 | 9.5 | 660 | 300 | 240 | 1 | 0.04 | 0.92 | 0.93 | 0.78 | 0.97 |
Model | MAE | RMSE | |
---|---|---|---|
SSA-ELMAN | 3.93 | 4.93 | |
ELMAN | 55.75 | 64.54 | |
BP | 43.29 | 53.92 | |
Empirical Equation (1) * | 61.70 | 64.72 | |
Empirical Equation (2) | 67.86 | 92.01 |
Model | Height of WFFZ (m) | Proportionality Coefficient | Absolute Error (m) | Relative Error (%) | |
---|---|---|---|---|---|
SSA-ELMAN | 124.9 | 23.51 | 1.1 | 0.87 | |
ELMAN | 168.9 | 31.87 | 42.9 | 34.05 | |
BP | 69.2 | 10.72 | 56.8 | 45.08 | |
Empirical Equation (1) | 166.8 | 31.47 | 40.8 | 32.38 | |
Empirical Equation (2) | 49.47 | 9.33 | 76.53 | 60.74 |
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Gao, X.; Liu, S.; Ma, T.; Zhao, C.; Zhang, X.; Xia, H.; Yin, J. A Prediction Method for Height of Water Flowing Fractured Zone Based on Sparrow Search Algorithm–Elman Neural Network in Northwest Mining Area. Appl. Sci. 2023, 13, 1162. https://doi.org/10.3390/app13021162
Gao X, Liu S, Ma T, Zhao C, Zhang X, Xia H, Yin J. A Prediction Method for Height of Water Flowing Fractured Zone Based on Sparrow Search Algorithm–Elman Neural Network in Northwest Mining Area. Applied Sciences. 2023; 13(2):1162. https://doi.org/10.3390/app13021162
Chicago/Turabian StyleGao, Xicai, Shuai Liu, Tengfei Ma, Cheng Zhao, Xichen Zhang, Huan Xia, and Jianhui Yin. 2023. "A Prediction Method for Height of Water Flowing Fractured Zone Based on Sparrow Search Algorithm–Elman Neural Network in Northwest Mining Area" Applied Sciences 13, no. 2: 1162. https://doi.org/10.3390/app13021162
APA StyleGao, X., Liu, S., Ma, T., Zhao, C., Zhang, X., Xia, H., & Yin, J. (2023). A Prediction Method for Height of Water Flowing Fractured Zone Based on Sparrow Search Algorithm–Elman Neural Network in Northwest Mining Area. Applied Sciences, 13(2), 1162. https://doi.org/10.3390/app13021162