王东, 杜涵, 王前领. 基于系统聚类-加权马尔科夫耦合模型滑坡预警方法研究与应用[J]. 煤炭学报, 2020, 45(5). DOI: 10.13225/j.cnki.jccs.2019.0607
引用本文: 王东, 杜涵, 王前领. 基于系统聚类-加权马尔科夫耦合模型滑坡预警方法研究与应用[J]. 煤炭学报, 2020, 45(5). DOI: 10.13225/j.cnki.jccs.2019.0607
WANG Dong, DU Han, WANG Qianling. Research and application for early warning of landslides based on hierarchical clustering coupling weighting Markov Chain model[J]. Journal of China Coal Society, 2020, 45(5). DOI: 10.13225/j.cnki.jccs.2019.0607
Citation: WANG Dong, DU Han, WANG Qianling. Research and application for early warning of landslides based on hierarchical clustering coupling weighting Markov Chain model[J]. Journal of China Coal Society, 2020, 45(5). DOI: 10.13225/j.cnki.jccs.2019.0607

基于系统聚类-加权马尔科夫耦合模型滑坡预警方法研究与应用

Research and application for early warning of landslides based on hierarchical clustering coupling weighting Markov Chain model

  • 摘要: 滑坡的非线性运动规律及演化过程中表现出的随机性态一直是滑坡灾害预测研究的难点之一。 针对现有滑坡预警理论与方法仍存在的可信度较低、错误率高及预警不及时等问题,基于马尔科夫链理论和系统聚类基本思想,视滑坡过程中的位移速度演变为马尔科夫过程,运用聚类分析将GPS速度观测信号转换为状态信号,从随机过程角度对滑坡位移加速度a>0这一滑坡判据进行了新的描述。 认为当监测获得的前日、当日位移速度以及预测获得的次日位移速度状态均为“异常”,且动态样本的位移速度均值和标准差持续增大时,预示着滑坡即将发生;在兼顾应规避滑坡预警滞后性的同时,还考虑了预警模型的降噪能力及可信度等3方面需求,提出了以预警区域敏感度、预警正确率与预测共识率为评估函数来检验滑坡预警模型的正确性和有效性,进而提出了系统聚类-加权马尔科夫链耦合滑坡预警方法;结合平庄西露天煤矿顶帮“4·17”滑坡工程实例,以GPS表面位移监测数据为训练集和测试集,对该滑坡预警方法的合理性进行了验证。 研究表明:采用该滑坡预警方法,不仅可及时、准确的确定滑坡预警时刻,还可体现滑坡启动、发生、发展在区域上的动态演变过程;对于平庄西露天煤矿的“4·17”滑坡,最佳样本容量为20,此时的平均7 d预警敏感度为84%,对全部监测点的期望预警正确率为93%,平均预测共识率为76%,能够满足工程需要。

     

    Abstract: One of the difficulties in landslide research can be always classified into two main categories:the nonlinear motion law of landslide and its stochastic state in the process of evolution. However,some existing landslide early-warn- ing methods and theories have low credibility,high error rate prediction,and untimely early warning. As can be seen, the displacement velocity progress in the landslide evolution can be regarded as a Markov process based on Markov chain theory and the basic idea of hierarchical clustering. Hierarchical clustering can convert GPS velocity observation signals to state signals. The model proposes a new description of acceleration larger than 0 of landslide displacement from the perspective of stochastic process. It is considered that when the monitoring displacement velocity state of the day,the day before,and the predicting displacement of the next day are all abnormal,and the mean and standard devi- ation of the dynamic sample displacement velocity continue to keep increasing,it heralds that slope is about to slide and then form a landslide. Considering the anti-interference and reliability of early warning model with respect to time- liness requirement,the paper presented an evaluation function to test the effectiveness of landslide early warning model by early warning sensitivity,early warning accuracy rate,and prediction consensus rate. Also,the landslide early warn- ing method of weighted Markov chain coupling hierarchical clustering has been put forward. The early warning model was tested with Pingzhuang open-pit mine engineering example. GPS surface displacement monitoring data was taken as the training dataset and validation dataset. The disquisition demonstrates that the early warning method proposed in the paper not only determines landslide early warning time,but also much reflects the promoter,occurrence,and pro- gress of landslide in the region of dynamic evolution. For the 4. 17 landslide engineering example in Pingzhuang open- pit mine,the optimal sample size is 20,the average 7-day landslide early warning sensitivity is 84% ,average early warning accuracy rate is 93% ,and the average prediction consensus rate is 76% . The parameter can meet the engi- neering requirements.

     

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