## 4 Discussion  
As landslide monitoring technology becomes more automated, and data volume and type become more enriched, more focus has been put on how to use automated monitoring data to quickly and accurately identify the landslide acceleration process and then carry out early warning work. In this paper, a hybrid early warning algorithm based on the parameters of cumulative displacement, velocity, acceleration, and normalized tangent angle is proposed for automated monitoring data from landslides, which primarily includes threshold determination, parameter selection, and early warning criterion judgment. The method was then tested using the Lishanyuan landslide as a case study.  
The proposed method combines the conventional warning method with the critical sliding warning method according to different strategies. The conventional warning method relies on the characteristics of cumulative displacement, velocity, acceleration, and the given thresholds to determine the warning level. This method requires almost no prior  
Figure 10: Early warning results of the LISHAN-DB02 monitoring station obtained by the traditional difference method. (**a**) Conventional warning based on velocity and acceleration (**b**) critical sliding warning based on the normalized tangent angle.  
parameters and can be used throughout the monitoring and warning process, but threshold selection is more difficult and requires dynamic adjustment throughout the monitoring period. Although this paper provides a semi-empirical and semi-quantitative method, there is no way to provide an early warning of the landslide's critical sliding stage, and it can only serve as a backup in this stage. In contrast, the critical sliding warning method uses the normalized tangent angle as a criterion, which requires the velocity \(v_{m}\) of the CDS to perform the normalization operation. The calculation and selection of \(v_{m}\) necessitate a longer period of monitoring, so it can't be used for a long time after the monitoring begins. Combining these two methods according to different strategies has two main advantages: Firstly, the hybrid of two methods can cover the entire monitoring life cycle with the identification and early warning of a landslide's accelerated deformation processes. Secondly, different hybrid strategies can be selected based on different scenarios, balancing the needs for immediacy, accuracy, and computational resources of early warning.  
The differential calculation approach studied by [PERSON] et al. [13,14] and [PERSON] et al. [10] may lead to significant oscillations and distortions in parameters such as velocity, acceleration, and normalized tangent angle, resulting in a large number of false warnings for automated monitoring data with characteristics such as large volume, unequal collection intervals, more anomalous data, and serious data fluctuations. To overcome this challenge, this study offers calculating various parameters using least-squares regression of the data inside a time window. This calculation approach not only uses substantially more data than the traditional difference calculation method, but it also has a noticeable continuous smoothness, which reduces the number of false warnings even further.  
In conclusion, the early warning method proposed in this paper can not only identify a landslide's accelerated deformation processes quickly and accurately using automated monitoring data throughout the monitoring life cycle, but it can also flexibly take into account user requirements for immediacy, accuracy, and computational resources of the early warning system using different strategies. However, many aspects of the method proposed in this paper need to be improved. For example, the determination of some of these parameters still relies on experience. Future optimization is needed to combine the evolution mechanisms of multiple physical fields inside the landslide.