基于矩阵奇异值分解约束型无迹粒子滤波的滑坡位移预测模型研究
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摘要
[目的]通过对滑坡位移预测模型进行研究,为政府部门实施更加可靠的灾害防治决策提供科学依据。[方法]提出了一种矩阵奇异值分解(SVD)约束型无迹粒子滤波(IUPF)方法,建立基于位移参数的滑坡位移预报模型。[结果]利用改进的SVD方法有效提升了无迹粒子滤波方法中Sigma点计算的鲁棒性,从而提升了算法的预测精度,对滑坡的稳定趋势能够做出更准确的预报。将该算法在镇江市跑马山滑坡体监测工程、京港澳高速公路雨花互通南侧护坡体滑坡监测工程相关数据进行了应用和分析验证。[结论]实例验证结果表明,加入SVD约束后的无迹粒子滤波算法,能够使得滑坡位移预测更加准确,预测的数据更加准确地反映了滑坡的变形趋势。
[Objective]To study the landslide displacement prediction model,in order to provide a scientific basis for government departments to implement more reliable disaster prevention and control decisions.[Methods]An iterative unscented particle filter (IUPF) method based on singular value decomposition (SVD) constrain was proposed to establish a landslide displacement prediction model based on displacement parameters.[Results]The SVD method was effectively improved the robustness of Sigma point calculation in the unscented particle filtering method,thereby improving the prediction accuracy of the algorithm and making a more accurate prediction of the landslide stability trend.The algorithm was applied to the application and analysis of the data related to the monitoring project of the Paomashan landslide in Zhenjiang City and the landslide monitoring project on the south side of the Yuhua Interchange in Beijing-Hong Kong-Macao Expressway.[Conclusion]The prediction in landslide displacement with the unscented particle filtering algorithm with SVD constraint can be more accurate,and the predicted data can reflect the deformation trend of the landslide more accurately.
[Objective]To study the landslide displacement prediction model,in order to provide a scientific basis for government departments to implement more reliable disaster prevention and control decisions.[Methods]An iterative unscented particle filter (IUPF) method based on singular value decomposition (SVD) constrain was proposed to establish a landslide displacement prediction model based on displacement parameters.[Results]The SVD method was effectively improved the robustness of Sigma point calculation in the unscented particle filtering method,thereby improving the prediction accuracy of the algorithm and making a more accurate prediction of the landslide stability trend.The algorithm was applied to the application and analysis of the data related to the monitoring project of the Paomashan landslide in Zhenjiang City and the landslide monitoring project on the south side of the Yuhua Interchange in Beijing-Hong Kong-Macao Expressway.[Conclusion]The prediction in landslide displacement with the unscented particle filtering algorithm with SVD constraint can be more accurate,and the predicted data can reflect the deformation trend of the landslide more accurately.
引文
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[12]刘勇,余宏明,刘烽博,等.滑坡位移非线性时间序列预测模型研究[J].地质科技情报,2016(5):203-207.
[13]姜娇娇,郭俊,杨淑莹.基于粒子滤波的混沌时间序列局域多步预测[J].现代电子技术,2018(1):43-46.
[14]de Freitas J F G.Bayesian methods for neural networks[D].Cambridge,UK:Department of engineering,Cambridge University,1999.
[15]付杰.卡尔曼滤波在滑坡变形预测中的应用研究[D].武汉:中国地质大学,2013.
[16]刘超云,尹小波,张彬.基于Kalman滤波数据融合技术的滑坡变形分析与预测[J].中国地质灾害与防治学报,2015,26(4):30-35.
[17]Merwe R V D,Doucet A,Freitas N D,et al.The unscented particle filter[C]∥International Conference on Neural Information Processing Systems.MIT Press,2000:563-569.
[18]Aharon M,Elad M,Bruckstein A.SVD:An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation[M].New York:IEEE Press,2006.
[19]郑杨.镇江市跑马山滑坡监测及预警预报技术研究[D].南京:南京大学,2017.
[2]国土资源部.全国地质灾害防治“十三五”规划[R].北京:国土资源部,2016.
[3]杜娟,殷坤龙,柴波.基于诱发因素响应分析的滑坡位移预测模型研究[J].岩石力学与工程学报,2009,28(9):1783-1789.
[4]周超,殷坤龙,曹颖,等.基于诱发因素响应与支持向量机的阶跃式滑坡位移预测[J].岩石力学与工程学报,2015(S2):4132-4139.
[5]彭令,牛瑞卿,吴婷.时间序列分析与支持向量机的滑坡位移预测[J].浙江大学学报:工学版,2013,47(9):1672-1679.
[6]王珊珊.滑坡位移预测中的时序数据挖掘研究[D].湖北宜昌:三峡大学,2015.
[7]陈卫兵,王德厚.相空间局域预测法在滑坡位移预测中的应用[J].长江科学院院报,2005,22(6):6-9.
[8]许春青.滑坡预测预报模型比较分析[D].哈尔滨:哈尔滨工程大学,2011.
[9]徐峰,汪洋,杜娟,等.基于时间序列分析的滑坡位移预测模型研究[J].岩石力学与工程学报,2011,30(4):746-751.
[10]曾耀,李春峰.基于RBF多变量时间序列的滑坡位移预测研究[J].长江科学院院报,2012,29(4):30-34.
[11]张俊,殷坤龙,王佳佳,等.基于时间序列与PSO-SVR耦合模型的白水河滑坡位移预测研究[J].岩石力学与工程学报,2015,34(2):382-391.
[12]刘勇,余宏明,刘烽博,等.滑坡位移非线性时间序列预测模型研究[J].地质科技情报,2016(5):203-207.
[13]姜娇娇,郭俊,杨淑莹.基于粒子滤波的混沌时间序列局域多步预测[J].现代电子技术,2018(1):43-46.
[14]de Freitas J F G.Bayesian methods for neural networks[D].Cambridge,UK:Department of engineering,Cambridge University,1999.
[15]付杰.卡尔曼滤波在滑坡变形预测中的应用研究[D].武汉:中国地质大学,2013.
[16]刘超云,尹小波,张彬.基于Kalman滤波数据融合技术的滑坡变形分析与预测[J].中国地质灾害与防治学报,2015,26(4):30-35.
[17]Merwe R V D,Doucet A,Freitas N D,et al.The unscented particle filter[C]∥International Conference on Neural Information Processing Systems.MIT Press,2000:563-569.
[18]Aharon M,Elad M,Bruckstein A.SVD:An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation[M].New York:IEEE Press,2006.
[19]郑杨.镇江市跑马山滑坡监测及预警预报技术研究[D].南京:南京大学,2017.