Geomechanics and Engineering

  • 제23권1호
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  • Pages.51-59
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  • 2020
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
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DOI QR Code

Development and application of a floor failure depth prediction system based on the WEKA platform

  • Lu, Yao (College of Energy and Mining Engineering, College of Safety and Environmental Engineering, Shandong University of Science and Technology) ;
  • Bai, Liyang (Lvliang University) ;
  • Chen, Juntao (College of Energy and Mining Engineering, College of Safety and Environmental Engineering, Shandong University of Science and Technology) ;
  • Tong, Weixin (College of Energy and Mining Engineering, College of Safety and Environmental Engineering, Shandong University of Science and Technology) ;
  • Jiang, Zhe (College of Energy and Mining Engineering, College of Safety and Environmental Engineering, Shandong University of Science and Technology)
  • 투고 : 2020.07.05
  • 심사 : 2020.09.07
  • 발행 : 2020.10.10
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, the WEKA platform was used to mine and analyze measured data of floor failure depth and a prediction system of floor failure depth was developed with Java. Based on the standardization and discretization of 35-set measured data of floor failure depth in China, the grey correlation degree analysis on five factors affecting the floor failure depth was carried out. The correlation order from big to small is: mining depth, working face length, floor failure resistance, mining thickness, dip angle of coal seams. Naive Bayes model, neural network model and decision tree model were used for learning and training, and the accuracy of the confusion matrix, detailed accuracy and node error rate were analyzed. Finally, artificial neural network was concluded to be the optimal model. Based on Java language, a prediction system of floor failure depth was developed. With the easy operation in the system, the prediction from measured data and error analyses were performed for nine sets of data. The results show that the WEKA prediction formula has the smallest relative error and the best prediction effect. Besides, the applicability of WEKA prediction formula was analyzed. The results show that WEKA prediction has a better applicability under the coal seam mining depth of 110 m~550 m, dip angle of coal seams of 0°~15° and working face length of 30 m~135 m.

키워드

과제정보

The research described in this paper was financially supported from the funding of the Taishan Scholar Talent Team Support Plan for Advantaged & Unique Discipline Areas and Key research and development plan of Shandong Province (2018GSF117018; 2018GSF120003; 2019GSF111024), Shandong Provincial Natural Science Foundation (ZR2019BEE013) and National Natural Science Foundation of China(51804179; 51604167; 51974173), State Key Laboratory of Mining Disaster Prevention and Control Co-founded by Shandong Province and the Ministry of Science and Technology(MDPC2016ZR01).

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