Study on Height Prediction of Water Flowing Fractured Zone in Deep Mines Based on Weka Platform

Liyang Bai, Changlong Liao, Changxiang Wang (), Meng Zhang, Fanbao Meng, Mingjin Fan and Baoliang Zhang
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Liyang Bai: Department of Mining Engineering, Lyuliang University, Lvliang 033001, China
Changlong Liao: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232001, China
Changxiang Wang: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232001, China
Meng Zhang: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232001, China
Fanbao Meng: School of Mechanicas Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Mingjin Fan: School of Mechanicas Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Baoliang Zhang: School of Architecture & Civil Engineering, Liaocheng University, Liaocheng 252000, China

Sustainability, 2022, vol. 15, issue 1, 1-15

Abstract: Accurately predicting the height of water flowing fractured zone is of great significance to coal mine safety mining. In recent years, most mines in China have entered deep mining. Aiming at the problem that it is difficult to accurately predict the height of water flowing fractured zone under the condition of large mining depth, the mining depth, height mining, inclined length of working face and coefficient of hard rock lithology ratio are selected as the main influencing factors of the height of water flowing fractured zone. The relationship between various factors and the height of water flowing fractured zone is analyzed by SPSS software. Based on the data mining tool Weka platform, Bayesian classifier, artificial neural network and support vector machine model are used to mine and analyze the measured data of water flowing fractured zone, and the detailed accuracy, confusion matrix and node error rate are compared. The results show that, the accuracy rate of instance classification of the three models is greater than 60%. The accuracy of the artificial neural network model is the highest and the node error rate is the lowest. In general, the training effect of the artificial neural network model is the best. By predicting engineering examples, the prediction accuracy of the model reaches 80%, and a good prediction effect is obtained. The height prediction system of water flowing fractured zone is developed based on VB language, which can provide a reference for the prediction of the height failure grade of water flowing fractured zone.

Keywords: water flowing fractured zone; Weka platform; Bayes classifier; neural network; support vector machine (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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