## 2 Case Study
### Deformation Monitoring Data of the Dawuchang Landslide  
The primary monitoring parameters for the Dawuchang landslide include surface displacement, precipitation, reservoir water level, and macroscopic inspection data. Surface displacement is measured using an automated Global Navigation Satellite System (GNSS) monitoring method, with a total of six monitoring stations and one reference station arranged to form two longitudinal monitoring profiles. Precipitation is recorded using an automated rain gauge station, with one rain monitoring point installed in the middle to rear section of the landslide. Reservoir water level variations are documented through data collection. The basic information on the various monitoring points within the landslide is presented in Table 1, while the monitoring network layout is illustrated in Figure 1.  
Since June 2016, the cumulative horizontal displacement duration curve of six automated GNSS surface displacement monitoring points (WS0102-WS0107) on the landslide body of Dawuchang is shown in Figure 1, and the cumulative horizontal displacement duration curve and the cumulative vertical displacement duration curve is shown in Figures 2 and 3. It can be seen that the cumulative horizontal displacement of each monitoring point since 2016 is 9.4-121.9 mm, and the cumulative vertical displacement is \(-\)33.8\(--\)10.4 mm. The average horizontal displacement rate was 0.1-1.5 mm/month, and the average vertical displacement rate was \(-\)0.4\(--\)0.1 mm/month. From the perspective of space, there is obvious deformation in the right posterior edge of the landslide, and the deformation in other parts is small or there is no obvious deformation, which is a sliding landslide. In terms of time, the landslide of Dawuchang has obvious deformation from July to October every year, which is consistent with the rapid decline of reservoir water level or extreme rainfall events (heavy rainfall, continuous rainfall). It can be seen that the main influencing factor of landslide deformation in Dawuchang is rainfall, followed by reservoir water level. Especially in the case of rainfall, the groundwater permeability pressure rises sharply, and a large amount of surface water converges to the landslide area, which further reduces the shear strength of the soil in the slide zone and leads to the aggravation of landslide deformation.  
Predicting the overall deformation trend in landslides in advance and judging their stable state is helpful to reduce the risks that landslides may bring to human life, property, and infrastructure. The monitoring data should first be pre-processed before making the forecast. In this paper, the method based on 3\(\sigma\) detection combined with Isolation Forest is used to eliminate the abnormal values existing in the daily monitoring data series. Subsequently, for the vacancy data, the KNN algorithm is used for interpolation. This method can make use of historical proximity observations to compensate for time-series breakpoints. In interpolation, only the displacement variable is used to measure  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Monitoring Content** & **Monitoring Method** & **Monitoring Point Number** & **Profile** & **Location** & **Elevation (m)** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Landslide \\ surface \\ absolute \\ displacement \\ \end{tabular} } & \begin{tabular}{c} \
\begin{tabular}{c} WS0101 \\ WS0102 \\ \end{tabular} &
\begin{tabular}{c} On stable bedrock \\the distance, and the data are standardized before interpolation to unify the dimension and improve the accuracy of interpolation in order to further improve the stability of the algorithm and provide a reliable time-series data basis for subsequent landslide warning and mechanism analysis.