## 1 Introduction  
Mountains and hills make up more than 60% of the total area of Hunan province in China, half of which have slopes greater than 25\({}^{\circ}\)[1]. This area has high rainfall, so landslide disasters are frequent. According to statistics, 2449 various geological disasters occurred in Hunan Province in 2020, causing economic losses of 262.49 million RMB, of which 2116 were landslide disasters, accounting for 86.4% [2]. Deploying multiple types of sensors on landslides to gather information on deformation, rainfall, stress, and other physical parameters, and providing timely warning, are low-cost and reliable prevention methods that can effectively reduce casualties [3; 4; 5]. With the development of sensor technology and Internet of Things technology, landslide monitoring is gradually developing towards the direction of automation and intelligence [6; 7; 8; 9]. It is of great significance to fully mine extensive monitoring data and extract and identify warning precursors for studying the mechanisms of landslide disasters and improving the accuracy of warning. Early and accurate identification of landslide precursors is a prerequisite for early warning. The traditional precursors that can be used for early warning are mainly macroscopic phenomena such as surface cracks, slope toe uplift and other macro phenomena [10; 11; 12]. With the development of monitoring technology, landslide precursors can be mined from abundant monitoring data, of which the most widely used type of data is surface deformation. The accelerated deformation process of landslides is the most intuitive and reliable precursor, so it is widely used in the study of landslide early warning. [PERSON] et al. [5; 13] proposed to use the normalized tangent angle as an indicator for early warning of landslides. [PERSON] et al. [14] proposed to use displacement-velocity ratio as an indicator for landslide warning. [PERSON] et al. [15] proposed a multicriteria approach to identify accelerated deformation processes in landslides. [PERSON] et al. [16] proposed a hybrid warning algorithm that could identify the landslide acceleration process quickly, automatically, and accurately in an online monitoring and warning system, and achieved the balance of warning immediacy, accuracy, and computational resources through different strategies. Although displacement, as a precursor of landslide disaster, can give early warning quickly and accurately, it also has many shortcomings. First, the current sensors for displacement monitoring are highly susceptible to environmental influences and often generate false alarms during the warning process [16; 17; 18]. Second, displacement is the result of a combination of multiple factors, both internal and external to the landslide. The acceleration of displacement foreshadows the initiation of the landslide process, and the warning window is very short [19; 20; 21]. Finally, the use of a single displacement characteristic for early warning does not take into account the impact of external trigger factors such as rainfall, earthquakes, and construction on the disaster, and is therefore necessarily incomplete. The development of data mining technology in recent years has provided new research ideas for landslide precursor identification. Data mining technology can filter and analyze useful information and important events from massive data to reveal the internal relationships and hidden rules of data, which have been widely used in the commercial [22; 23], industrial [24; 25], engineering [26; 27], medical [28; 29; 30] and educational [31; 32] fields with remarkable effect. The application of data mining techniques in the field of landslides is mainly focused on susceptibility assessment [33; 34; 35], aiming to analyze landslide instability at the regional scale, while there are very few studies on application in specific landslide monitoring. [PERSON] et al.