## 4 Different Landslide Prediction Methods
### Landslide Monitoring Methods  
Landslide monitoring is necessary for early detection of landslides as well as early warning. It detects changes in attribute values of landslide triggering variables and records slope displacements at probable landslide sites to minimize landslide-induced damage. The development of slope stability models relies heavily on the monitoring of kinematic, hydrological, and climatic data. Forecasting is impossible without a thorough knowledge  
Figure 14: Monte Carlo simulation used for landslide analysis [51].  
of movement patterns and reactions to climatic events [53; 54]. In the physically based model technique, the parameters are not treated as categorical variables in the probabilistic analysis technique. In previous studies, probabilistic analytic techniques were employed to evaluate physically based models but not the hydrogeological model, implying a consistent level of water across the study area [55]. A GPS, tiltmeter, and wire extensometer were installed in the upper part of the landslide to determine the shape and magnitude of the sliding masses [56]. Using displacement monitoring, the authors tried to estimate the absolute longitudinal and lateral motions, as well as the speed of the various components of the complicated landslide. Inclinometers have been used in a number of studies to track the progress of landslides [57]. This restriction is nearly entirely addressed by remote sensing techniques, which use sensors that can detect the earth's geological characteristics from a distance and without direct contact. This, along with recent significant advancements in space- or airborne-sensing platforms, has resulted in a significant increase in the contribution of remote sensing to landslide risk analysis, early warning and monitoring [58]. Aerial photo-interpretation, which was historically carried out by calibrated optical cameras flying on airplanes, depending on high-resolution images of the ground, was the first RS tool employed in geomorphological research, with a particular reference to landslide identification and mapping (both in grayscale and RGB) [59]. Traditional airborne photogrammetry was the first technique to be surpassed by space-borne optical image analysis, mostly due to cost considerations [60]. The transition from conventional human interpretation and mapping to semi-automated or completely automated interpretation and mapping is essential for the use of optical images in landslide monitoring. In practice, if the revisiting time is short enough to allow for monitoring, the image elaboration must be as quick [59]. According to ALS, which investigated the morphology of two huge landslides in Idaho (USA) at various spatial scales, mass movement geometry and kinematics are minimized by using geomorphometry with high resolution DEMs, such as laser scanners. A number of factors, including aircraft altitude, LiDAR quality, topographic surface layout, and vegetation effects, affect the feasibility of DEMs and 3D models produced by ALS [61]. Because of the difficult terrain and extensive vegetation in the mountains of western China, inSAR displacement estimates are inherently more unreliable. The accuracy of landslide detection may be compromised if there is insufficient validation. Landslides were first detected using stacking InSAR technology over a vast region in this research. Then, high-resolution DEM and aerial LiDAR data were used to validate, alter, and precisely delineate the borders of active landslides in a narrow zone, as shown in Figure 15.  
Figure 15: Mapping and characterization of LiDAR [62].