## 1 Introduction  
Landslide hazard is one of the most common geological hazards in the natural world. They are also directly affected by human engineering activities. China is one of the countries most affected by landslide disasters in the world [1]. After the Ms 8.0 Wenchuan earthquake on 12 May 2008, tens of thousands of landslides over a broad area in west China were triggered. Some of these buried large areas of the town and dammed the rivers [2]. Thus, how to reduce property damage and casualties has always been an urgent problem. Landslide early warning systems (EWSs), which have already been working in many places of the world, were developed to fulfil this work [3; 4; 5; 6; 7]. According to the definition of the United Nations International Strategy for Disaster Reduction (UNISDR 2009), an EWS is defined as \"the set of capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities and organizations threatened by a hazard to prepare and to act appropriately and in sufficient time to reduce the possibility of harm or loss.\" Referring to the above definitions, an efficient landslide EWS should comprise of four main sets of actions [8]: (1) monitoring activities, i.e., data acquisition, transmission, and maintenance of the instruments; (2) analysis and modelling of the phenomenon; (3) warning, i.e., the dissemination of simple and understandable information to the exposed elements; and (4) an effective response of the elements exposed to risk and the risk knowledge. Among the four sets of actions, (1) and (2) are critical for the successful early warning of an EWS. In the regional scale landslide EWSs, a statistical method is used to determine the warning threshold. These landslide EWSs monitor the rainfall and make a classification early warning according to the rainfall threshold and the soil moisture [1, 9, 10, 11, 12, 13, 14]. Regarding a single landslide EWS, an early warning is made according to the induced factors and movement characters of the landslide [15]. Different instruments are used in single landslide EWSs to measure the induced factors and movement characters, for example, an inclinometer for tilt [16, 17], fiber Bragg grating for fissures [18], an acoustic emission instrument for inner displacements [19, 20], Ground-Based Synthetic-Aperture Radar, LiDAR(Light Detection and Ranging), total station, GPS and photogrammetric techniques for surface displacements [15, 21, 22, 23, 24], a geoelectrical monitor for soil moisture [25], and a wire extensometer for rock fracture [26]. These measuring data can be used to make early warnings with a single model or integrated models [27, 28]. The majority of these models are derived from geological models. They usually make a classified early warning which is a qualitative analysis [18, 26, 27, 28, 29]. In this paper, we attempt to use the precursor features, specifically the vibration frequency, to carry out a quantitative analysis of landslide prediction. The Kalman filtering (KF)-fast Fourier transform (FFT)-support vector machine (SVM) model is proposed, which can build a grey box model for real-time early warning based on displacement data. The KF-FFT-SVM model uses Kalman filtering (KF) to make predictions, fast Fourier transform (FFT) to extract precursor features, and the support vector machine (SVM) method to classify the precursor features. To ensure the KF-FFT-SVM model worked well, we fed it with real-time monitoring data. This required a stable and reliable monitoring system, which is especially important in the case of an emergency. In practice, most of the existing landslide monitoring systems are susceptible to breaking down in the field environment [30]. The measuring station was built with concrete which needs several days to harden, so it usually takes some more time to build a monitoring system, which therefore cannot immediately begin monitoring if a landslide suddenly occurred. Thus, the FDMS was designed.