LAUSR

Mining induced land subsidence is one of the most hazardous geological phenomenon. Predictive modeling of the ground subsidence has attracted increased interest and is crucial to the hazard prevention. In this research, a data-driven approach integrated with survival analysis to model the mining-induced subsidence is studied. The data used in this research is collected in Fuxin, Liaoning Province, China and it contains multiple variables from different subsided locations. First, a survival analysis is conducted using the Cox proportional hazard model to evaluate the importance of variables considered. p values of all variables are computed and the important variables are selected. Next, data-driven models including k-nearest neighbor, support vector machine, back-propagated neural network, random forest, extreme learning machine, and online sequential extreme learning machine are constructed to predict the subsidence values and horizontal movement. Two evaluation matrices namely MAPE and RMSE are introduced to evaluate the performances of the data-driven models. Computational results demonstrate that online sequential extreme learning machine is capable of accurately predict the mining induced subsidence and surface deformation.