GM-SVM Prediction Model for Land Subsidence at Finished Underground Mining and its Application
Underground mining is one of the causes of land subsidence. The process of the land subsidence caused by underground raining is complicated and systematicness. Accurate prediction the land subsidence has important practical or immediate significance to avoid the harm of land subsidence. Grey system theory was applied extensively and had gained a series of achievements, but our preliminary study show that the general CM (1, 1) model was inadequate to handle land subsidence prediction as its only adapt to the data with exponential law. The advantages and disadvantages of grey forecasting methods and support vector machine (SVM) are analyzed respectively, this article proposes a new land subsidence settlement forecasting model of grey support vector machine. The new model develops the advantages of accumulation generation in the grey forecasting method, weakens the effect of stochastic disturbing factors in original sequence, strengthens the regularity of data. The example shows that the prediction accuracy has been improved quite a lot in comparison with general grey model.
grey theory support vector machine land subsidence underground mining
Xie Zhengwen Hu Hanhua Li Shuqing
Safety and environment protection research institue, China Jiliang University, Hang Zhou 310018, Chi School of Resources and safety Engineering, Central South University, Changsha 410083, China
国际会议
The 3rd International Symposium on Modern Mining & Safety Technology Proceedings(第三届现代采矿与安全技术国际学术会议)
辽宁阜新
英文
1000-1004
2008-08-04（万方平台首次上网日期，不代表论文的发表时间）