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Geomechanics and Engineering Volume 23, Number 1, October10 2020 , pages 51-59 DOI: https://doi.org/10.12989/gae.2020.23.1.051 |
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Development and application of a floor failure depth prediction system based on the WEKA platform |
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Yao Lu, Liyang Bai, Juntao Chen, Weixin Tong and Zhe Jiang
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| Abstract | ||
| 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 0o~15o and working face length of 30 m~135 m. | ||
| Key Words | ||
| floor failure depth; WEKA platform; the grey relational degree; optimal model; prediction system | ||
| Address | ||
| Yao Lu, Juntao Chen and Weixin Tong: 1.) College of Energy and Mining Engineering, College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China 2.) State Key Laboratory of Mining Disaster Prevention and Control Co-Founded by Shandong Province and the Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao 266590, China Liyang Bai: Lvliang University, Lvliang, 033000, Shanxi, China Zhe Jiang: 1.) College of Energy and Mining Engineering, College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China 2.) Lvliang University, Lvliang, 033000, Shanxi, China | ||