Safety prerequisite for coal mine using Wireless Sensor Technique and Artificial Intelligence Technology

Amar Nath Singh, Bikash Narayan Naik


Coal mines are the great source of goods for which we are highly dependent. It plays a vital role for the development of growth of nation. Here in this paper we are focusing over the safety prerequisite for the coal mines. As we know that now a day’s Wireless sensor networks (WSN) and the Modern Artificial Intelligence technique are good at security monitoring in coal mine[2]. By using these techniques we can easily detect diverse parameters, which can reduce human and material losses. In Coal mine we usually found  pivotal parameters which include Dust, Temperature, Wind Speed (WindS), various poisons Gases. So before the mining we should also look for the safety prerequisites for the miners. Here in this paper we have taken some referential data and by utilizing that we have tried to increase the  effectiveness of the proposed approach.


C. Zhuang, H. Y. Wang, C. Q. Fu and S. M. Zhuang, “Data Integration Technology-based Safety Supervisory System Information Transmission Strategy”, International Journal of Advancements in Computing Technology, vol. 3, no. 10, (2011), pp. 23-29.

X. C .Li, “Coal Mine Safety in China”, China Coal Industry Press, Beijing, China, (1998).

M. Li and Y. H. Liu, “Underground Coal Mine Monitoring with Wireless Sensor Networks”, ACM Transactions on Sensor Networks, vol. 5, no. 2, (2009), pp. 10-29.

Environment Australia (2002) “Overview of Best PracticeEnvironmental Management in Mining.”

MINEO Consortium (2000) “Review of potential environmentaland social impact of mining”

U.S. Environmental Protection Agency, Title 40 Code of Federal Regulations, Section 70.2. pkg/CFR-2009-title40-vol15/xml/CFR-2009-title40-vol15- part70.xml

J. Michael Friedel, “Modeling hydrologic and geomorphic hazards across post-fire landscapes using a self-organizing map approach”, Environmental Modeling & Software, vol. 26, (2011), pp. 1660-1674.

R. Kothari and S. Islam, “Spatial characterization of remotely sensed soil moisture data using self organizing feature maps”, IEEE Transactions on Geoscience and Remote Sensing, vol. 37, (1999), pp. 1162-1165.

S. W. Wang, X. Y. Xu, Q. Tang, M. Liu and J. S. Yu, “A Study on Eco-Hydrology Regionalization and Its Application”, Proceedings of 4th International Conference on Bioinformatics and Biomedical Engineering, (2010), pp. 1-6.

K. Nishiyama, S. Endo, K. Jinno, C. B. Uvo, J. Olsson and R. Berndtsson, “Identification of typical synoptic patterns causing heavy rainfall in the rainy season in Japan by a Self-Organizing Map”, Atmospheric Research, vol. 83, no. 2-4, (2007), pp. 185-200.

R. Kumar, M. Wolenetz and B. Agarwalla, “DFuse: A Framework for Distributed Data Fusion”, Proceedings of ACM SENSYS, (2003), pp. 114-125.

R. N. Handcock, D. L. Swain and G. J. Bishop-Hurley, “Monitoring Animal Behavior and Environmental Interactions Using Wireless Sensor Networks, GPS Collars and Satellite Remote Sensing”, Sensors, vol. 9, no. 5, (2009), pp. 3586-3603.

S. Mohamad, M. S. Vahid and R. Shabnam, “Application of self organizing map (SOM) to model a machining process”, Journal of Manufacturing Technology Management, vol. 22, no. 6, (2011), pp. 818-830.

T. Kohonen, “The self-organizing map”, Proc. of the IEEE, (1990), pp. 1464-1480.

T. Kohonen, “Self Organizing Maps”, Third, Extended Edition 2001, Springer, Berlin, (1995).

A. Hentati, A. Kawamura, H. Amaguchi and Y. Iseri, “Evaluation of sedimentation vulnerability at small hillside reservoirs in the semi-arid region of Tunisia using the Self-Organizing Map”, Geomorphology, vol. 122, no. 1-2, (2010), pp. 56-64.

Full Text: PDF [Full Text]


  • There are currently no refbacks.

Copyright © 2013, All rights reserved.|

Creative Commons License
International Journal of Science Engineering and Advance Technology is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJSEat , Permissions beyond the scope of this license may be available at