## 1 Introduction  
Natural disasters, such as earthquakes, tsunamis, volcanoes, landslides, ground subsidence, debris flows, and pit collapses, can be extremely harmful to human societies. In terms of scale, earthquakes and tsunamis are violent and dangerous and pose a great threat to human beings, but due to their quantity, landslides are the second most serious threat. Approximately 8000 human casualties can be attributed to landslides each year, with direct economic losses exceeding $1 billion. In China alone, 640,000 km\({}^{2}\) land is covered by loess [1], which is loose, porous, collapsible, and has a weak load-bearing capacity, resulting in a risk of landslides, debris flows, and other disasters. In addition, landslides have become more frequent due to earthquakes, and increases and decreases in the groundwater level, rainfall, mining, and other factors. For example, the Wenchuan earthquake occurred on 12 May 2008 [2], and caused a number of large landslides, resulting in heavy human casualties. The Three Gorges Dam is in internationally renowned engineering project because of its high water storage capacity, which has resulted in changes in the groundwater level. Changes in rainfall amounts, groundwater levels, and weak sliding surfaces have increased the occurrence of landslides [3]. The occurrence of geological disasters such as landslides often leads to immense losses to human societies, and have; therefore, attracted much attention from governments worldwide, as well as experts and researchers in related fields. Many studies have been conducted on the early identification, inducement, characteristics, classification, prevention, and control of landslides, including stability analyses, spatial and temporal predictions, and early warning and forecasting. Several studies have used satellite remote sensing images, interferometric radar (InSAR), laser ranging (LIDAR), unmanned aerial vehicles (UAVs) low-altitude remote sensing images, field investigations, and other methods to identify the distribution and slope features of landslides [4; 5; 6; 7]. Other studies have applied finite element, discrete element, and other numerical simulation methods as well as material simulation experiments to conduct stress-strain analyses and determine the causes of instability at the time of slope failure [8; 9; 10]. To better apply their results to engineering practice, researchers have classified landslides into peristaltic landslides [11], rainfall induced landslides [12], mining landslides, and seismic landslides [13]. Because theoretical studies of landslides have been conducted in China, the measures and methods used to respond to landslides are improving, and the state's funds allocated for disaster prevention and control have increased over time. However, China is unable to comprehensively control all landslides and potentially unstable slopes under the present conditions, and it is therefore necessary to ensure the prediction and early warning of landslide disasters and reduce the losses associated with them. Previous attempts to develop slope failure early warning systems have mainly been conducted through field surveys and investigations, studies of stratigraphic geomorphology to statistically analyze the possibility of a landslide occurrence, or through the installation of observation stations, displacement meters, and depth meters to obtain monitoring data. This has been followed by the application of a variety of predictive models, such as the grey model (GM), polynomial regression model, machine forest model, logical regression model, strength reduction method, and back propagation (BP) neural network [13; 14; 15; 16]. In recent years, with the expansion in the application of geographic information systems (GIS) and the maturity of satellite differential positioning technology, Global Navigation Satellite System (GNSS) real-time monitoring systems have made a breakthrough in the field of landslide early warning and prediction [17; 18].