Prediction of Mining-induced Ground Subsidence Using Support Vector Regression
(0) This study applies support vector regression techniques to develop empirical analysis model for estimation of mining-induced ground subsidence. The highly nonlinear relationship between the subsidence coefficient and affecting factors including qualitative and quantitative factors, such as mining parameters, rock mass mechanical properties, engineering geological conditions, and other relevant aspects was regressed from the field data. Typical cases collected from 30 coal mines located in different parts of China are used to train the support vector regression model and the trained model is then applied for prediction analysis to examine the efficiency of the current method. The application results show great performance of support vector algorithm which provides a promising alternative for prediction and prevention of mining-induced ground subsidence.
ground subsidence support vector regression nonlinear relationship prediction coal mining
YANG Chengxiang FENG Xiating
School of Resources & Civil Engineering, Northeastern University, Shenyang 110004, China
国际会议
The 2007 International Symposium on Safety Science and Technology(2007采矿科学与安全技术国际学术会议)
河南焦作
英文
242-247
2007-04-17（万方平台首次上网日期，不代表论文的发表时间）