###### Abstract  
Landslides are frequent and catastrophic geological hazards, and forecasting their movement is an important aspect of risk assessment and engineering prevention. Based on the integrated deep displacement three-dimensional measuring sensor with sensing unit array structure, an improved multivariable grey model based on dynamic background value and multivariable feedback is proposed to build predictive models for the evolutionary condition of landslides. In the modeling process, the traditional grey model was replaced by extracting the trend information of each variable, instead of summing up each independent variable after assigning weights to it, besides, the Whale Optimization Algorithm (WOA) is used to modify the default value in the model's background variables. By predicting more than 1000 sets of deep displacement monitoring data collected in the landslide simulation test conducted at the landslide simulation test device, the displacement prediction accuracy of our purposed model is 26%, 47%, and 87% respectively higher than the optimizing grey model (OGM) for three sensing units at different depths. Moreover, a new landslide risk assessment approach based on the orientation vector angle is proposed to make stability discriminations which is less susceptible to volatile data than the TOPSIS-Entropy weight theory and avoids the problem of lack of uniform standards due to the complexity of environmental factors. grey model; time series prediction; deep displacement; landslide; sensor +
Footnote †: journal: Medical  
Many instruments and techniques have been used to monitor the slope surface displacement, including GPS technology [6; 7; 8], geodetic method [9; 10; 11; 12], and Interferometric Synthetic Aperture Radar technology [13; 14; 15], etc. At present, surface displacement monitoring technology has been developed to a relatively high level, which generally has the characteristics of stable performance, high precision, automatic real-time monitoring and so on. However, since the surface displacement monitoring cannot reflect the deformation characteristics of the deep rock and soil mass, it does not meet the needs of stability analysis and management engineering design in many cases. For example, GPS technology can only monitor the displacement or settlement of the site where the sensor is located, and the measurement accuracy is vulnerable to weather [16; 17], meanwhile, the Interferometric Synthetic Aperture Radar technology is difficult to monitor the landslide displacement covered with luxurian trees and needs unmanned aerial vehicles (UAVs) to assist, which is complex to operate and cannot be monitored in real-time [18]. Compared with the surface displacement monitoring technology, the complexity of the monitoring environment leads to slower development of deep displacement monitoring technology. The borehole inclinometer is the most widely used technology, which has been proven capable of monitoring the occurrence of landslides in advance [19; 20]. However, it is low-efficiency because manual reading is required for each measurement, worse yet, along with the continuous evolution of the landslide deformation, the inclinometer tube in the borehole is vulnerable to being broken or extruded, which will make the sliding inclinometer unable to sink into the borehole for further measurement. Time-domain reflectometry [21; 22; 23] is another remote electronic measurement technology that can be used to monitor the displacement but this method cannot determine the direction of landslide movement, nor can it be used in rock and soil mass without shear force. With the development of technology, a new type of displacement monitoring instrument, Fiber Bragg Grating technology [24; 25], has been developed. Even though this technology has the features of anti-electromagnetic interference, long transmission distance, it is sensitive to temperature and easy to break during large deformations [26].