Abstract：Based on induced ordered weighted averaging (IOWA) operator, we combine the difference autoregressive integrated moving average (ARIMA) model and Holt-Winters exponential smoothing model. We use the SBAS-InSAR monitoring value to predict mining area surface subsidence, and compare this prediction with the results of each single model. The results show that the prediction accuracy of the combined model based on IOWA operator is significantly improved compared with that of the single model. For the combined model, the mean square error (MSE) and the mean absolute error (MAE) of each point reaches 1.458 mm and 2.175 mm respectively, which can be used for the monitoring and prediction of mining area surface subsidence.
ZHOU Wentao,ZHANG Wenjun,YANG Yuanji et al. A Combined Model Prediction Method for Surface Subsidence Monitoring in Mining Areas[J]. jgg, 2021, 41(3): 308-312.
