矿区地表沉降监测的一种组合模型预测方法

基于诱导有序加权平均（IOWA）算子，将差分整合移动平均自回归（ARIMA）模型和Holt-Winters指数平滑模型进行组合，采用SBAS-InSAR监测值进行矿区地表沉降预测，并与各单一模型的预测结果进行对比分析。结果表明，基于IOWA算子的组合模型的预测精度较单一模型有明显提升，其中各点均方误差（MSE）平均值为1.458 mm，平均绝对误差（MAE）为2.175 mm，可用于矿山地表沉降监测预测。

A Combined Model Prediction Method for Surface Subsidence Monitoring in Mining Areas

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.