###### Abstract
When sufficient monitoring data are obtained, various mathematical methods can be used to predict the landslide displacement relatively accurately. These methods can be classified into two categories. The first one is the model-driven prediction methods [27; 28], mainly based on geographic andomorphic conditions and general creep theories. However, such methods are usually complex and have many limitations. On the one hand, they can only be used for these landslides that have occurred, and the final states are necessary to be known so as to calculate the intermediate process of disasters. On the other hand, each model can only be used for a specific process, which lacks robustness. The other is the data-driven prediction model method on the basis of historical data, which is more widely used in engineering practice. It does not need to take too many geographical factors into account that it can be widely used in different cases. Various methods were developed in previous research, such as the information value model [29; 30; 31; 32], regression model [33; 34], grey model [35; 36; 37; 38], random forests [39; 40; 41], and artificial neural network [42; 43; 44]. These methods can be divided into small-sample methods and large-sample methods based on the sample size. The large sample method is often more widely used. In addition to displacement monitoring data, large sample methods usually require monitoring data such as precipitation, soil moisture, water level, etc. Machine learning is a typical representation of the large sample method. It can consider the nonlinear behaviors and correlations in the historical monitoring data of different variables. However, in complex field monitoring scenarios, due to the failure of monitoring instruments or human factors, the actual landslide monitoring data may have large noise and data loss, resulting in inaccurate prediction results. Small sample prediction is often used for data samples with stable, exponential, and convergent characteristics, and the grey model is the most common mathematical model in small sample landslide prediction. Under the same sample size, the computational complexity of the grey model is significantly smaller than that of artificial neural networks, extreme learning machines, and deep learning machines. Since landslides are non-integral movements in most cases, the displacement usually first appears in the interior of the rock and soil mass and gradually passes upward to the surface, so it is more practical to evaluate the stability and early forecast of landslides through deep displacement monitoring. In order to overcome these questions mentioned above, in previous work, a new deep displacement measuring sensor based on dual mutual inductance [45] has been developed. The whole sensor adopts a sensing array structure, thus, the sensor can deform synchronously with the surrounding rock and soil mass, and the distance and direction of the sensing unit are approximately equal to the displacement of the surrounding rock and soil mass. Each sensing unit contains an air-core coil, a magnetic-core coil, associated circuitry, and software. It has the advantage of flexible structure design, automatic measurement, and high measurement accuracy. More importantly, it can reflect the three-dimensional displacement change of deep displacement of rock and soil mass. In this work, due to the long collection time of the entire sensor, it is impossible to use large sample methods to predict. Therefore, an improved grey model to predict the displacement of landslides and a new method to determine the stability of slopes are proposed, so as to make full use of the monitoring data from the new deep displacement monitor sensor. As this sensor is still in the testing stage, it is not suitable to apply it to the field. Thus, we have built a landslide simulation experiment platform to simulate the landslide occurrence process, so as to check the monitoring performance of the entire sensor. The sequence of the article can be summarized as follows.