Abstract:

According to the characteristic of the road collapse data in mined-out area，a new method based on empirical mode decomposition (EMD) and weighted least squares support vector machines (WLS-SVM) has been put forward to forecast the ground subsidence and applied into the coal-mining-induced collapse prediction of Changping high way in Jilin Province．Three steps has been adopted to deal with the measured collapse data: Firstly, to gain the smooth signal curve by cubic spline interpolation method. Secondly, to use EMD to deal with the time-space filtering and noise reduction to obtain the residual component which shows the collapse trend. At last, to input the dealt data to WLS-SVM model and then get the result of collapse prediction. The final collapse value has been predicted to be 174.34 cm, the average deviation of the final collapse prediction results using WLS-SVM is about 1.06%. Comparing the measured data in situ and the predicted data by WLS-SVM and BP neural network, the result shows that the suggested method has relatively high forecast accuracy and can be applied widely．

Key words: cubic spline interpolation, EMD, WLS-SVM, mined-out area, subsidence