## 5 Discussion  
Landslide EWSs are widely used to reduce disaster losses. The effectiveness of a landslide EWS depends on the reliability of monitoring and accurate early warnings. In this paper, we focused on the monitoring and warning methods of a landslide EWS. Effective responses after the landslide are beyond the discussion of this paper. To improve the robustness of the monitoring system of a landslide EWS, we designed the FDMS. With the real-time monitoring landslide displacement data obtained by the FDMS, we used the KF-FFT-SVM model to predict landslide slope failure with high accuracy. The prediction method we proposed here could forecast the landslide in advance, and is a timely online prediction method.  
Although the real-time prediction method proposed here showed a good result in the Baige landslide, there are still limitations in practice. The precursory features of different landslides may be different, and thus the trained SVM model cannot be effective for all type of landslides. The SVM model should be trained by the critical slide data of the landslide; however these critical slide data are difficult to obtain as there may be no monitoring equipment working wherever a landslide occurs, or if the equipment is destroyed after the landslide slope failure.  
In this study, we were lucky to obtain the critical slide data to train the KF-FFT-SVM model. To make the model effective for more landslides, we should use as many historical critical slide data as possible to train the model and adjust the model parameters for different types of landslides. There are also uncertainties in the application of the real-time prediction method. The most important feature of this method is that it uses the surface displacement data to find the precursory features. The problem is that, in the KF-FFT-SVM model, we consider the surface displacement data measured by the Beidou receivers as the mechanical vibration of the landslide, but there must be distortions when the vibration signals are transmitted to displacement signals.  
The method proposed in this study provides a new idea for the real-time early warning of landslides. In this method, a landslide is considered as a quadratic dynamical system, and we extracted the precursory features from the surface displacement data of the landslide and used machine learning to construct the early warning model. By introducing the concept of quadratic dynamical systems and machine learning, the real-time prediction method makes the landslide early warning easier, and the only thing needed is to train the model with plenty of data.  
The real-time prediction method is also practical and has strong promotion value. The majority of the landslide EWSs in the literature have very complex monitoring systems and make an early warning simply by a pre-set threshold, which produces a lack of accuracy and timeliness [27; 28]. In the real-time prediction method, all the displacement data obtained by different instruments can be used to extract precursory features and train the KF-FFT-SVM model. In this study, we only used the surface displacement data, and this method would be even more effective if the deep-seated information of the landslide is used.