Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things

Abstract

The international community has paid extensive attention to the numerous engineering problems faced by karst areas caused by the increasingly frequent human activities. China has a wide variety of karst forms. Among them, carbonate karst is the most widely distributed, and the development of carbonate karst is relatively strong in many areas. Countless property losses are caused by karst disasters every year. This article aims to study the real-time monitoring and timely early warning of karst collapse through the use of multisensor Internet of Things technology. To this end, this article proposes an improved method for multisensor data fusion. It optimizes and improves the transmission and delivery efficiency of its data. This makes the improved multisensor more in line with the research content of this article in terms of monitoring efficiency. At the same time, related experiments and analyses are designed to compare and analyze the karst collapse and the monitoring efficiency of the sensor. The experimental results of this article show that after the improvement, the anti-interference ability of the monitoring system is increased by 34%. The frequency of early warning has also been improved by 24%, which has high practical application value.

Monitoring Methodology Study of a Reservoir Built on the Karst Depression: A Large Scale Laboratory Simulation Test

“…The development of a karst collapse is closely correlated with the air–water pressure in the underground karst channel. , To monitor the karst collapse hazard caused by groundwater activities in rock formations, groundwater level and water–air pressure sensors are placed in karst cavities in karst areas to monitor groundwater level data and water–air pressure. − Pan et al monitored the groundwater variation process in a closed mine in a karst area, and they found many sinkhole collapses were caused by the groundwater level fluctuation . In terms of air pressure monitoring, many techniques were developed including an automatic monitoring method of the water–air pressure sensor, − dynamic real-time groundwater monitoring, − and Internet of Things technology, − which have been used in field monitoring and for early warning of karst collapse. , Jiang, Lei, and Zhao developed the specific air pressure monitoring techniques for karst collapse monitoring and used them to monitor many field karst collapses. , They collected a large amount of air pressure data caused by groundwater level fluctuation, which is useful in karst collapse predication. Overall, the groundwater activity can lead to high air–water pressure and induce karst collapse.…”

“… 6 − 8 Pan et al monitored the groundwater variation process in a closed mine in a karst area, and they found many sinkhole collapses were caused by the groundwater level fluctuation. 9 In terms of air pressure monitoring, many techniques were developed including an automatic monitoring method of the water–air pressure sensor, 10 − 12 dynamic real-time groundwater monitoring, 13 − 16 and Internet of Things technology, 17 − 19 which have been used in field monitoring and for early warning of karst collapse. 20 , 21 Jiang, Lei, and Zhao developed the specific air pressure monitoring techniques for karst collapse monitoring 22 and used them to monitor many field karst collapses.…”

Retracted: Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”