## 1 Introduction  
The southern region of China has undulating terrain and abundant rainfall, so it suffers from many geological disasters. From 2014 to 2019, a total of 46,957 geological disasters occurred in China, of which 33,386 were landslides, accounting for more than 70% of total geological disasters [1]. Nearly 90% of these landslides occurred in southern China, causing huge casualties and economic and property losses. Compared with the high-cost landslide treatment, monitoring and giving early warning of landslides with potential sliding risks is more economical and effective [2; 3; 4; 5]. For the early warning, we should obtain the deformation parameters (surface displacement, deep displacement, crack width), changes in rock and soil parameters (water content, earth pressure), and other factors (groundwater level, rainfall, etc.). A growing number of studies have focused on how to effectively and comprehensively use monitoring data to predict landslide failure in recent years [2; 5; 6]. A large number of landslide deformation monitoring results show that most soil slopes and some rock slopes experienced creep with different deformation characteristics before failure [7]. Such a creep feature is particularly useful for early warning because there is enough time for data collection and analysis. Many early warning and prediction methods are based on this feature [8; 9; 10; 11]. The monitoring results of many rainfall-inducedlandslides and landslides close to reservoirs in recent years show that the displacement curves of such landslides show obvious \"step-like\" characteristics, with an acceleration process in the rainy season every year and then plateauing in the dry season, which makes the accurate prediction of landslide failure time more difficult. Despite this, the early warning can be obtained as long as each acceleration in the landslide deformation process is accurately grasped. This is the core of the method proposed in this article. Many studies are also based on this idea. Ref. [12] presented the application of an automatic procedure specifically developed to identify the onset of landslide acceleration by analyzing displacement monitoring data with a multi-criteria approach. [PERSON]. [13; 14] suggested a new method to carry out the phase division and to explore tangential angular features of the displacement-time curve of creep slopes, as well as the acceleration characteristics in the process of slope deformation and the pre-warning criteria for critical failure. Ref. [15] proposed a new method based on the inverse logistic function considering inverse distance weighting to predict the displacement of landslides, and the quantitative standards of dividing the deformation stages and determining the critical sliding time are put forward. All of these methods have been widely used in actual landslide cases and can accurately identify the landslide acceleration process. With the rapid development of monitoring sensors and Internet of Things technology, landslide monitoring has gradually developed from manual to automated [16; 17; 18; 19; 20]. The automated monitoring system for landslides includes four parts: the perceptual layer used to sense the deformation, mechanics, water level, rainfall, and other physical conditions of the landslide site; the data layer used to receive and store the monitoring data and provide a data access interface; the service layer used to process and analyze monitoring data and provide application programming interfaces for the terminal layer; and the terminal layer used to provide human-computer interaction interfaces. The perceptual layer is the basis of the entire system. It is composed of various sensors, equipment for data acquisition and communication, and supporting systems including batteries and solar panels deployed on the landslide site.