Integration of D-InSAR technology and PSO-SVR algorithm for time series monitoring and dynamic prediction of coal mining subsidence
Integration of D-InSAR technology and PSO-SVR algorithm for time series monitoring and dynamic prediction of coal mining subsidence
复制标题
D-InSAR技术与PSO-SVR算法融合进行煤矿沉陷时间序列监测与动态预测
DOI：
10.1179/1752270614y.0000000126
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发表时间：
2014-10
期刊：
影响因子：
1.6
通讯作者：
B. Q.
中科院分区：
文献类型：
--
作者：
Chen;B. Q.
摘要煤矿开采引起的地表塌陷是许多国家面临的严重环境问题。因此，必须在矿区建立有效的监测预报系统，以保护附近的财产和周围环境。提出了一种将差分干涉合成孔径雷达(D-InSAR)技术与支持向量回归(SVR)算法相结合的开采沉陷监测与动态预测模型。首次利用D-InSAR技术监测开采沉陷的影响范围和发展趋势，从而获得地表沉陷规律。基于D-InSAR技术获得的监测结果，采用SVR算法描述监测数据与未来沉降量之间的非线性函数相关性。由于支持向量机算法的性能在很大程度上取决于相关参数的选择，因此引入粒子群优化算法来选择支持向量机算法的最优参数。最后，采用一种基于优化后的支持向量机参数的滚动预测方法来更新支持向量机的训练和学习样本，从而允许算法利用最新的监测数据来动态预测未来的开采沉陷。为了验证所提方法的适用性，将其应用于中国内蒙某煤矿区，从2012年11月21日至2013年4月2日在该矿区采集了13幅TerraSAR-X图像。实验结果表明，监测结果非常准确地反映了开采沉陷的影响范围和发展趋势，PSO-SVR算法提供了高精度的预测结果，最大绝对误差(MAE)为29 mm，最大相对误差(MRE)为6.5%，证明了该模型的准确性和可行性。
Abstract Subsidence of the ground surface caused by coal mining is a serious environmental problem in many countries. Therefore, an effective monitoring and prediction system must be established in coal mining areas to protect nearby property and the surrounding environment. In this paper, a model is proposed that integrates differential interferometry synthetic aperture radar (D-InSAR) technology and the support vector regression (SVR) algorithm to monitor and dynamically predict mining subsidence. D-InSAR technology is first used to monitor the range of influence and the development trend of mining subsidence, thus obtaining the law of surface subsidence. Based on the monitoring results obtained by D-InSAR technology, the SVR algorithm is used to describe the nonlinear function correlativity between the monitored data and future subsidence. As the performance of the SVR algorithm depends largely on the choice of relevant parameters, the particle swarm optimisation (PSO) algorithm is introduced to select the optimal parameters for the SVR algorithm. Finally, a method of rolling prediction based on the optimised SVR parameters is adopted to update the training and learning samples of SVR, thus allowing the algorithm to use the latest monitored data to dynamically predict future mining subsidence. To verify the applicability of the proposed methodology, it was applied to a coal mining area in Neimeng, China, where thirteen TerraSAR-X images were acquired from 21 November 2012 to 2 April 2013. The experimental results show that the monitoring results very accurately reflect the range of influence and the trend in the development of mining subsidence and also that the PSO-SVR algorithm provides high-accuracy prediction results with a maximum absolute error (MAE) of 29 mm and a maximum relative error (MRE) of 6·5%, thus demonstrating the accuracy and feasibility of the proposed model.
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DOI：
10.1007/978-3-319-44427-7
发表时间：
2016
期刊：
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影响因子：
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作者：
通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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DOI：
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发表时间：
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期刊：
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影响因子：
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通讯作者：
Marco Dorigo;M. Birattari;Xiaodong Li;Manuel López-Ibáñez;Kazuhiro Ohkura;Carlo Pinciroli;T. Stützle
DOI：
10.1007/978-3-030-00533-7
发表时间：
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期刊：
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影响因子：
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作者：
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Marco Dorigo;M. Birattari;Christian Blum;Anders Lyhne Christensen;A. Reina;V. Trianni