Research on Coal Seam Floor Water Inrush Monitoring Based on Perception of IoT Coupled with GIS


To the complication and uncertainty in coal seam floor water-inrush monitoring, Internet of Things (IoT) perception is applied to the monitoring and controlling of coal seam floor water inrush with major impacting factors analyzed, and an open distribution information processing platform is constructed based on IoT-GIS coupling perception. Then using the platform to comprehensively perceive various floor water inrush impacting parameters, an AHP model is established. At this stage, by means of weight reasoning algorithm based on dynamic Bayesian network, the AHP weight can be worked out using the two-way probability transfer and chain rules. Then the multiple factors are spatially fused by GIS to form a non-linear mathematical model for the calculation of the water inrush relative probability index. After that, the discrimination threshold of the comb graph for the floor water inrush relative probability index is used to further identify the floor water inrush mode. The experiments in 10# Coal Seam of Suntuan Mine show that, the accuracy perceived the floor water inrush is above 92%, and the platform of IoT-GIS coupling perception has the obvious technical advantage than traditional monitoring technology. Therefore, it has has demonstrated strong systematic robustness, important theoretical and application significance.

Share and Cite:

X. Meng, J. Wang and Z. Gao, "Research on Coal Seam Floor Water Inrush Monitoring Based on Perception of IoT Coupled with GIS," Engineering, Vol. 4 No. 8, 2012, pp. 467-476. doi: 10.4236/eng.2012.48061.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] L. Li and J.-L. Cheng, “Floor Water Irruption Prediction Based on Information Fusion,” Journal of China Coal Society, Vol. 31, No. 5, 2006, pp. 623-626.
[2] Y.-L. Li, “A Case Study of Methods and Application of Yaoqiao Mine Water Disaster Based on Multi-Information Fusion Assessmen,” China University of Mining and Technology, Beijing, 2010.
[3] D. S. Zhang, S. S. Zheng, Y. J. Sun, et al., “GIS in Forecasting Coal Mine Flood,” China University of Mining and Technology Press, Xuzhou, 1994.
[4] H.-S. Zhang, G.-W. Xue, X.-W. Shi, et al., “Prediction of Water Inrush from Coal Seam Floor by Means of a Geo-Information Composite Overlay Analysis,” Journal of China Coal Society, Vol. 34, No. 8, 2006, pp. 1100-1104.
[5] J. A. Wang and H. D. Park, “Coal Mining above a Confined Aquifer,” Rock Mechanics & Mining Sciences, Vol. 40, No. 4, 2003, pp. 537-551. doi:10.1016/S1365-1609(03)00029-7
[6] W. Liao, R.-Y. Zhou and S.-Q. Li, “A Study of the Nonlinear Forecasting Method for Prediction of Water Inrush from Coal Floor by means of the Wavelet Neural Network,” China Safety Science Journal, Vol. 16, No. 11, 2006, pp. 24-28.
[7] J.-S. Qian, S.-S. Ma and Y.-J. Sun, “Designing of on the Comprehensive Automatic Mining System by Means of the Internet of Things,” Coal Science and Technology, Vol. 39, No. 2, 2011, pp. 73-76.
[8] S. Zhang, E.-J. Ding, Z. Xu, et al., “The Second Lecture on Internet of Things and Mine Sensing—Mine Sensing, Digital Mining and Integrated Automatic Mining,” Industry and Mine Automation, Vol. 11, 2010, pp. 129-132.
[9] C.-C. Zhang, “The Theories and Methods for Spatial Analysis in GIS,” Wuhan University Press, Wuhan, 2004.
[10] W.-T. Liu, W.-Q. Zhang and J.-X. Li, “An Evaluation of the Safety of Floor Water Irruption Using Analytic Hierarchy Process and Fuzzy Synthesis Methods,” Journal of China Coal Society, Vol. 25, No. 3, 2000, pp. 278-282.
[11] I. Tsamardinos, E. Brown and C. F. Aleferis, “The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm,” Machine Learning, Vol. 65, No. 1, 2006, pp. 31-78. doi:10.1007/s10994-006-6889-7
[12] N. Friendman and D. Koller, “Being Bayesian about Network Structure: A Bayesian Approach to Structure Discovery in Bayesian Networks,” Machine Learning, Vol. 50, No. 1-2, 2003, pp. 95-125. doi:10.1023/A:1020249912095
[13] T. L. Saaty, “The Analytic Hierarchy Process,” McGraw-Hill, New York, 1980.
[14] D.-W. Jin, Y.-F. Liu, H. Feng, et al., “Development and Application of Monitoring and Early Warning System for Seam Floor Water Inrush,” Coal Science and Technology, Vol. 39, No. 11, 2011, pp. 14-17.
[15] Z. P. Meng, B. B. Zhang, X. T. Xie, et al., “Evaluation of Water Inrush Risk of Seam Floor Based on Iithology-Structure,” Coal Geology & Exploration, Vol. 39, No. 5, 2011, pp. 35-40.
[16] Q.-K. Cao and F. Zhao, “Forecast of Water Inrush Quantity from Coal Floor Based on Genetic Algorithm-Support Vector Regression,” Journal of China Coal Society, Vol. 36, No. 12, 2011, pp. 2097-2101.
[17] Q. Wu, W. Pang, Y.-C. Dai, et al., “The Technique by Coupling of GIS with ANN as Applied to the Evaluation of the Vulnerability of the Model for Forecasting the Floor Groundwater Bursting,” Journal of China Coal Society, Vol. 31, No. 3, 2006, pp. 314-319.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.