## 2 Methods  
### The Proposed Hybrid Early Warning Method  
According to the monitoring results of many landslides with creep characteristics, the displacement evolution process before landslide failure is shown in Figure 1. The entire landslide creep process is divided into three parts according to the cumulative displacement: normal zone (F), initial attention zone (G), and key attention zone (H). Following the deployment of monitoring equipment, the monitoring data typically reaches a data plateau (A), after which the landslide accelerates and deforms (B) due to inducing factors such as rainfall and construction. It may then decelerate and stabilize, or it may be deformed at a constant velocity or continuously accelerated (C,D). This process may be repeated several times, until, at last, the landslide accelerates without deceleration (E) until failure. From this process, it can be found that accelerated deformation is the premise of landslide failure, so fast and accurate identification of each accelerated deformation process is the basis of landslide warning. This paper proposes a hybrid warning method based on the above creep landslide deformation patterns and automated monitoring data features, which can not only accurately identify the landslide acceleration process, but can also effectively reduce the number of false warnings, as shown in Figure 2. The initial empirical thresholds and parameters are given after the monitoring sensors are deployed on the unstable landslide to be monitored and warned, and then the warning cycle (the \"_warning cycle_\" section in Figure 2) begins,  
Figure 1: Refined landslide failure process. (**A**): Plateau stage, (**B**): Initial deformation stage, (**C**): Constant deformation stage, (**D**): Accelerated deformation stage, (**E**): Critical sliding stage, (**F**): normal zone, (**G**): initial attention zone, (**H**): key attention zone. formally starting the monitoring and warning of the landslide. In the \"warning cycle\" section, the warning service deployed on the servers goes through a continuous cycle of extracting monitoring data, preprocessing, warning judgment, and extracting new data, resulting in automated warnings. This cyclic process necessitates dynamic changes to various parameters and thresholds based on the monitoring situation and the need for early warning. Parts A-D in Figure 2 depict some key moments in the adjustment and processing processes. A particular quantity of monitoring data has been obtained after approximately a week of monitoring. To develop thresholds and parameters suited for the current landslide and update them in the early warning cycle, statistical calculations and empirical analysis of monitoring data are required. If the landslide's cumulative displacement exceeds \(S_{ka}\) and it enters the key attention zone, the CDS can be determined initially, followed by the normalized velocity, which can be used to calculate the normalized tangent angle, and the early warning strategy in the early warning cycle can be adjusted to meet different needs. As the monitoring process continues, dynamically adjusting thresholds, parameters, and strategies in response to monitoring and warning conditions are required. For each warning cycle, the outliers for the monitoring data within the current warning time window must first be identified and rejected. The early warning calculation and level judgment are then carried out. The warning is reported if it is not a green warning. Then, as soon as the next data moment arrives, the warning services move on to the next cycle. The challenges that the hybrid warning method suggested in this research aims to tackle are how to calculate the warning, determine the warning level, and adjust the warning thresholds, parameters, and strategies. Figure 2: The flowchart of the hybrid early warning method. There are two parts to the hybrid early warning method: conventional early warning and critical sliding early warning.