Water inrush evaluation of coal seam floor by integrating the water inrush coefficient and the information of water abundance

作     者：Shi Longqing Qiu Mei Wei Wenxue Xu Dongjing Han Jin 

作者机构：Shandong Provincial Key Laboratory of Depositional Mineralization & Sedimentary Minerals College of Earth Science and Engineering Shandong University of Science and Technology College of Information Science and Engineering Shandong University of Science and Technology 

出 版 物：《International Journal of Mining Science and Technology》 (矿业科学技术学报（英文版）)

年 卷 期：2014年第24卷第5期

页      面：677-681页

核心收录：

学科分类：0819[工学-矿业工程] 081903[工学-安全技术及工程] 08[工学] 

基　　金：Financial supports for this work, provided by National Natural Key Science Foundation of China (No. 50539080) Ministry of Education Research Fund for the doctoral program of China (No. 20133718110004) the Natural Science Key Foundation of Shandong Province of China (No. ZR2011EEZ002) the Technology Project Development Plan of Qingdao Economic and Technological Development Zone of China (No. 2013-1-62) SDUST Research Fund of China (No. 2012KYTD101) 

主　　题：Floor water inrush Water inrush coefficient Water abundance Units inflow Support vector machine 

摘      要：The method of singular coefficient of water inrush to achieve safety mining has limitation and one sidedness. Aiming at the problem above, large amounts of data about water inrush were collected. Then the data, including the maximum water inrush, water inrush coefficient and water abundance in aquifers of working face, were processed by the statistical analysis. The analysis results indicate that both water inrush coefficient and water abundance in aquifers should be taken into consideration when evaluating the danger of water inrush from coal seam floor. The prediction model of safe-mining evaluation grade was built by using the support vector machine, and the result shows that this model has high classification accuracy. A feasible classification system of water-inrush safety evaluation can be got by using the data visualization method which makes the implicit support vector machine models explicit.