基于EWT-Prophet方法的地表沉降预测

针对地表沉降预测研究中单一传统方法预测精度较低、预测过程不稳定等问题，提出一种经验小波变换（EWT）与Prophet预测模型相结合的优化预测方法。以江西省上饶市德兴矿区为例，采用30景哨兵1号影像进行SBAS-InSAR沉降研究，并获取该区域研究时段内的沉降时序数据。首先对原始沉降时序数据进行EWT自适应分解，分解产生经验尺度分量和一系列经验小波分量；然后对各分量进行Prophet预测后叠加重构，得到最终的沉降预测值；最后采用回溯性预测法验证本文方法的精度和可靠性。实验结果表明，EWT-Prophet组合模型整体优于单一Prophet模型及传统ARMA模型，相较于其他2种方法，EWT-Prophet模型的均方根误差分别提升51.53%和59.03%，平均百分比误差分别提升57.81%和64.85%，表明本文方法预测效果更佳，且具备较好的适用性，为大面积矿区沉降预测提供了一种有效方法。

Surface Subsidence Prediction Based on EWT-Prophet Method

We propose an optimized land subsidence prediction method combining empirical wavelet Transform(EWT)and Prophet prediction model to solve the problems of low prediction accuracy of single traditional method and unstable prediction process.Taking the Dexing mining area in Shangrao City,Jiangxi Province,as an example,using the 30-view sentinel No.1 image,we carry out the SBAS-InSAR subsidence study and obtain the time series data of the subsidence of the area in the study time span.This method first performs EWT adaptive decomposition on the original sedimentation time series data,decomposes to produce empirical scale components and a series of empirical wavelet components,performs Prophet prediction and superimposition reconstruction on each component to obtain the final predicted settlement data,and finally adopts retrospective prediction to verify the prediction accuracy and reliability of the proposed method.Experimental results show that the EWT-Prophet combined model is overall better than the single Prophet model and the traditional ARMA model.Compared with the other two methods,the EWT-Prophet model improves the root mean square error by 51.53%and 59.03%respectively and the average percentage error is increased by 57.81%and 64.85%respectively,indicating that the prediction effect of this method is better,has better applicability,and provides an effective method for the prediction of large-area mining area settlement.