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
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)
Gavin, H., Barbara, M.: Sustainable development in the mining industry: clarifying the corporate perspective. Resour. Policy 6, 227–238 (2000)
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)
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)
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)
Yan, C., Dai, H., Guo, W.: Evaluation of ecological environmental quality in a coal mining area by modelling approach. Sustainability 9(8), 1265 (2017)
Duzgun, H.S.B., Einstein, H.H.: Assessment and management of roof fall risks in underground coal mines. Saf. Sci. 42, 23–41 (2004)
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)
Wu, Y.X.: Application of Integrative Geophysical Exploration to the Coal Mine Disaster Prevention. Saf. Environ. Eng. 16(2), 95–100 (2010)
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)
Salmi, E.F., Nazem, M., Karakus, M.: Numerical analysis of a large landslide induced by coal mining subsidence. Eng. Geol. 217, 141–152 (2017)
Carrara, A., Cardinali, M., Detti, R.: GIS techniques and statistical models in evaluating landslide hazard. Earth Surf. Proc. Land. 16, 427–445 (1991)
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)
Chen, Z., Wang, J.: Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada. Nat. Hazards 42, 75–89 (2007)
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)
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)
Havenith, H.B., Torgoev, I., Meleshko, A.: Landslides in the Mailuu-Suu Valley, Kyrgyzstan—Hazards and impacts. Landslides 3, 137–147 (2006)
Xue, Y., Zhang, M., Li, J.: Research of 3S technology in monitoring geological disasters in coal-mining area. Disaster Adv. 5, 427–432 (2012)
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)
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)
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)
Onesto, V., Narducci, R., Amato, F.: The effect of connectivity on information in neural networks. Integr. Biol. 10, 121–127 (2018)
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)
Schmidhuber, J.: Deep learning in neural networks: an overview. Izv. Atmos. Oceanic Phys. 61, 85–117 (2015)
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)
Eliseo, S.M.: Enhanced direct least square fitting of ellipses. Biochim. Biophys. Acta (BBA) 61, 939–953 (2008)
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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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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