The surface residual deformation caused by coal mining activities may pose a potential threat to surface struc-tures,roads,underground pipelines,and other infrastructure.Accurate prediction of this deformation is necessary.This study proposes an improved predictive model using the Cuckoo Search algorithm to enhance Support Vector Machine regression(CS-SVM)based on the monitoring results of SBAS-InSAR.Sixty Sentinel-1A SAR images from November 2017 to June 2020 were utilized to monitor the long-term subsidence of the 7221 mining face in a coal mine in Anhui Province.The surface average an-nual deformation rates and cumulative deformation within 2 years after cessation of mining were obtained.The results indicate that the maximum average annual deformation rate of the mining face is-56 mm/a,and the maximum cumulative subsidence is 151 mm.Validation of the InSAR results was performed using leveling measurement data,and the residual values were both less than 5 mm,demonstrating good consistency between the two methods.To evaluate the prediction model's accuracy before and after optimization,the average absolute error and root mean square error were introduced as evaluation criteria.The results dem-onstrate that both errors of the optimized model were within 4 mm,representing a 59%and 60%reduction in errors compared to the traditional model,resulting in a significantly improved prediction accuracy.This study shows that the proposed method exhibits strong predictive capability and can serve as a reference for disaster prevention and mitigation in abandoned coal min-ing areas.
关键词
开采沉陷/SBAS-InSAR/沉陷监测/地表残余沉降/最优参数/预测模型
Key words
mining subsidence/SBAS-InSAR/subsidence monitoring/surface residual subsidence/optimal parameters/prediction model
维普
万方数据