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A Model Base Framework for the Risk Assessment and Prevention of Geological Disasters in Coal Mines

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Geo-informatics in Sustainable Ecosystem and Society (GSES 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 980))

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Abstract

To evaluate geological hazards in mines effectively and systematically, we proposed an object-oriented model base framework that realizes model management and model reuse. This framework supports model generation, data storage, operation, analysis, prediction and application and includes model building and model management. When building the model, 7 commonly used disaster assessment models are encapsulated as model classes and model instances that are represented as objects. Model management includes evaluation factor management, model addition, modification, deletion and so on. In addition, the framework makes full use of the spatial data processing capabilities of Geographic Information System (GIS) to perform spatial analysis and prediction. We also applied the framework to the serious exploitation area of a mine in the Fangshan District, Beijing. The results showed that the proposed model base has strong operability and practical value and could provide early warnings for the geological hazards of coal mine areas.

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References

  1. Mejía-Navarro, M., Wohl, E.E., Oaks, S.D.: Geological hazards, vulnerability, and risk assessment using GIS: model for Glenwood Springs. Geomorphology 10, 331–354 (1994)

    Article  Google Scholar 

  2. Gavin, H., Barbara, M.: Sustainable development in the mining industry: clarifying the corporate perspective. Resour. Policy 6, 227–238 (2000)

    Google Scholar 

  3. Chau, K.T., Sze, Y.L., Fung, M.K.: Landslide hazard analysis for Hong Kong using landslide inventory and GIS. Comput. Geosci. 30, 429–443 (2004)

    Article  Google Scholar 

  4. Su, Z.J., Wang, J., Wang, L.: Application of geophysical technology in prevention of mining geological disasters. Appl. Mech. Mater. 256–259, 2669–2672 (2013)

    Google Scholar 

  5. He, F., Gu, L., Wang, T.: The synthetic geo-ecological environmental evaluation of a coastal coal-mining city using spatiotemporal big data: A case study in Longkou, China. J. Cleaner Prod. 142, 854–866 (2016)

    Article  Google Scholar 

  6. Yan, C., Dai, H., Guo, W.: Evaluation of ecological environmental quality in a coal mining area by modelling approach. Sustainability 9(8), 1265 (2017)

    Article  Google Scholar 

  7. Duzgun, H.S.B., Einstein, H.H.: Assessment and management of roof fall risks in underground coal mines. Saf. Sci. 42, 23–41 (2004)

    Article  Google Scholar 

  8. Palei, S.K., Das, S.K.: Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: an approach. Saf. Sci. 47, 88–96 (2009)

    Article  Google Scholar 

  9. Wu, Y.X.: Application of Integrative Geophysical Exploration to the Coal Mine Disaster Prevention. Saf. Environ. Eng. 16(2), 95–100 (2010)

    Google Scholar 

  10. Yang, X.J., Hou, D.G., Hao, Z.L.: Fuzzy comprehensive evaluation of landslide caused by underground mining subsidence and its monitoring. Int. J. Environ. Pollut. 59, 684 (2016)

    Article  Google Scholar 

  11. Salmi, E.F., Nazem, M., Karakus, M.: Numerical analysis of a large landslide induced by coal mining subsidence. Eng. Geol. 217, 141–152 (2017)

    Article  Google Scholar 

  12. Carrara, A., Cardinali, M., Detti, R.: GIS techniques and statistical models in evaluating landslide hazard. Earth Surf. Proc. Land. 16, 427–445 (1991)

    Article  Google Scholar 

  13. Abbruzzese, J.M., Labiouse, V.: New Cadanav methodology for quantitative rock fall hazard assessment and zoning at the local scale. Landslides 11(4), 551–564 (2014)

    Article  Google Scholar 

  14. Chen, Z., Wang, J.: Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada. Nat. Hazards 42, 75–89 (2007)

    Article  Google Scholar 

  15. Mujabar, P.S., Chandrasekar, N.: Coastal erosion hazard and vulnerability assessment for southern coastal Tamil Nadu of India by using remote sensing and GIS. Nat. Hazards 69, 1295–1314 (2013)

    Article  Google Scholar 

  16. Arca, D., Citiroglu, H.K., Kutoglu, H.S.: Unsustainable urban development for Zonguldak metropolitan area (NW Turkey). Int. J. Sustain. Dev. World Ecol. 21, 398–405 (2014)

    Article  Google Scholar 

  17. Havenith, H.B., Torgoev, I., Meleshko, A.: Landslides in the Mailuu-Suu Valley, Kyrgyzstan—Hazards and impacts. Landslides 3, 137–147 (2006)

    Article  Google Scholar 

  18. Xue, Y., Zhang, M., Li, J.: Research of 3S technology in monitoring geological disasters in coal-mining area. Disaster Adv. 5, 427–432 (2012)

    Google Scholar 

  19. Wu, Q., Xie, K., Chen, Z.A.: catastrophe model on the evaluation and classification of mine disaster rescue measures. Syst. Eng. Procedia 4, 484–489 (2012)

    Article  Google Scholar 

  20. Bahar, I., Atilgan, A.R., Erman, B.: Direct evaluation of thermal fluctuations in proteins using a single-parameter harmonic potential. Fold Des. 2, 173 (1997)

    Article  Google Scholar 

  21. Charpiat, B., Bedouch, P., Dode, X.: Quantifying the amount of information available in order to prescribe, dispense and administer drugs. Br. J. Clin. Pharmacol. 77, 908–909 (2014)

    Article  Google Scholar 

  22. Onesto, V., Narducci, R., Amato, F.: The effect of connectivity on information in neural networks. Integr. Biol. 10, 121–127 (2018)

    Article  Google Scholar 

  23. Kiselev, B.V.: Studying randomness and determinism in surface temperature anomaly indices using the recurrence plot method. Izv. Atmos. Oceanic Phys. 52, 33–36 (2016)

    Article  Google Scholar 

  24. Schmidhuber, J.: Deep learning in neural networks: an overview. Izv. Atmos. Oceanic Phys. 61, 85–117 (2015)

    Google Scholar 

  25. Morris, A.M., Watzky, M.A., Finke, R.G.: Protein aggregation kinetics, mechanism, and curve-fitting: a review of the literature. Biochim. Biophys. Acta (BBA) 1794, 375–397 (2009)

    Article  Google Scholar 

  26. Eliseo, S.M.: Enhanced direct least square fitting of ellipses. Biochim. Biophys. Acta (BBA) 61, 939–953 (2008)

    Google Scholar 

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Acknowledgments

This work was supported in part by a grant from the National Science Foundation of China (41471330), the Primary Research & Development Plan of Shandong Province (2016GSF117017) and the National Key Technology R&D Program of the Ministry of Science and Technology (2012BAH27B04).

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Correspondence to Yong Sun .

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Sun, Y., Jin, F., Ji, M., Wang, H., Li, T. (2019). A Model Base Framework for the Risk Assessment and Prevention of Geological Disasters in Coal Mines. In: Xie, Y., Zhang, A., Liu, H., Feng, L. (eds) Geo-informatics in Sustainable Ecosystem and Society. GSES 2018. Communications in Computer and Information Science, vol 980. Springer, Singapore. https://doi.org/10.1007/978-981-13-7025-0_40

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  • DOI: https://doi.org/10.1007/978-981-13-7025-0_40

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7024-3

  • Online ISBN: 978-981-13-7025-0

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