ISSN 1003-8035 CN 11-2852/P
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XUE Lian,TANG Qiao,ZHENG Jie,et al. Dynamic threshold analysis method of early warning and forecast based on real-time geo-hazards monitoring data[J]. The Chinese Journal of Geological Hazard and Control,2023,34(4): 11-21. DOI: 10.16031/j.cnki.issn.1003-8035.202206009
Citation: XUE Lian,TANG Qiao,ZHENG Jie,et al. Dynamic threshold analysis method of early warning and forecast based on real-time geo-hazards monitoring data[J]. The Chinese Journal of Geological Hazard and Control,2023,34(4): 11-21. DOI: 10.16031/j.cnki.issn.1003-8035.202206009

Dynamic threshold analysis method of early warning and forecast based on real-time geo-hazards monitoring data

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  • Corresponding author:

    唐 侨(1986—),男,四川资阳人,硕士研究生,主要从事地质信息化,地质灾害预警预报研究。E-mail:472118028@qq.com

  • Received Date: June 14, 2022
  • Revised Date: July 14, 2022
  • Accepted Date: August 16, 2022
  • Available Online: May 11, 2023
  • Currently, in the field of geological disaster monitoring, monitoring and early warning information is mainly released based on the threshold setting of various types of monitoring equipment. However, since the thresholds are established according to empirical values or expert evaluations, there is a lack of pertinence to different types of geological disasters and different environments. Once set, these thresholds remain unchanged for a long time, and even when adjusted, they only slightly float based on experience, lacking scientific data sample analysis. Additionally, monitoring equipment is susceptible to satellite signals and environmental factors, leading to false alarms and false negatives during operation. To address these issues, a method of dynamic adjustment of self-learning and self-correction early warning thresholds is proposed. This method introduces two variable thresholds and a new performance index optimization method for VTAS based on priority and gate and semi-Markov processes. The application of the semi-Markov process allows the method to consider industrial measurements with non-Gaussian distributions. Moreover, an optimization design process based on genetic algorithms is proposed to improve performance indicators by optimizing parameter settings. Three numerical examples are used to illustrate the effectiveness of this approach and compare it with previous studies. When applied at the measured point, the method effectively reduce false alarms and under-alarms compared to the use of fixed thresholds, improving the accuracy of geological disaster early warning and better protecting the safety of people's lives and property.
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