Abstract
As important methods to avoid landslide disasters, velocity monitoring and early warning are significant research topics in slope engineering at the present stage. This paper combines the randomness of velocity data in evolution process of landslide disasters, using Markov chain theory with no aftereffect to describe the randomness process, and introduces it into landslide warning. The research collects velocity monitoring data before landslide occurrence and applies average standard deviation method which can reflect statistical characteristics of the classification data to carry out state division of the velocity data. Then, it proposes landslide warning criteria and establishes landslide warning model based on dynamic prediction of future velocity status by Markov chain theory. Meanwhile, it puts forward the evaluation standard of landslide warning model from the aspects of timeliness, anti-interference, and credibility. At the same time, it takes typical open-pit mine landslide disaster as the engineering background and gradually optimizes and evaluates the landslide warning model from the above three evaluation standards. The results show that the landslide warning model can realize the landslide early warning of multiple monitoring points; it has good effects in both time warning and regional warning. On the other hand, the landslide warning model has high accuracy in timeliness, anti-jamming, and credibility, and it can reveal space-time evolution law of landslide occurrence, so this research has important theoretical significance and engineering promotion value.
Highlights
As important methods to avoid landslide disasters, velocity monitoring and early warning are significant research topics in slope engineering; its core is monitoring data collection and landslide warning analysis
Yan quantified the influence of external environment on slope stability through sensitivity analysis and established a quantitative landslide warning model which considered the influence of external environment [17]
The error warning rate is high, that means the warning information is provided when there is no risk of landslides, and this error warning information will have a serious impact on the normal productions and lives of residents [22]. e reason is that, due to the complexity, randomness, and uncertainty of landslide disasters evolution, a warning model is only suitable for a certain type or a certain stage of landslide prediction, and various landslide warning models have certain limitations
Summary
En, it proposes landslide warning criteria and establishes landslide warning model based on dynamic prediction of future velocity status by Markov chain theory. As important methods to avoid landslide disasters, velocity monitoring and early warning are significant research topics in slope engineering at the present stage. It puts forward the evaluation standard of landslide warning model from the aspects of timeliness, antiinterference, and credibility. As important methods to avoid landslide disasters, velocity monitoring and early warning are significant research topics in slope engineering; its core is monitoring data collection and landslide warning analysis.
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