Description : Subsidence coefficient is a key parameter for ground movement and deformation prediction when mining under the building, water, and railway; so how to get exact subsidence coefficient is one of the most important problems in the discipline of mining subsidence. Support vector machine (SVM) is a new algorithm of machine learning based on statistical learning theory. Compared with traditional method, SVM can be established under condition of deficient samples and abnormal observation result can be rejected effectively. Based on comprehensive analysis of effect factors on subsidence coefficient such as mechanical characteristics of upper rock stratum, thickness of alluvium deposit, ratio value of mining deepness to thickness, mining method and roof control method, etc, data from tens of typical observation stations was used as training samples, by means of electing kernel function, insensitive loss function, proper penalty factor, regression relation model of SVM was designed between subsidence coefficient and affecting factors. Finally, testing and analyzing was done, and research results show that the SVM relation model can calculate subsidence coefficient and reliable precision can be got, which can meet the requirement of engineering. Research findings prove that the method to calculate subsidence coefficient based on SVM method is feasible. Besides, multiple effect factors can be comprehensively considered with this method, thus a new approach of efficient and accurate calculation of subsidence coefficient is provided for future research.
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