Abstract
Landslide early warning systems (EWSs) have been widely used to reduce disaster losses. The effectiveness of a landslide EWS depends highly on the prediction methods, and it is difficult to correctly predict landslides in a timely manner. In this paper, we propose a real-time prediction method to provide real-time early warning of landslides by combining the Kalman filtering (KF), fast Fourier transform (FFT), and support vector machine (SVM) methods. We also designed a fast deploying monitoring system (FDMS) to monitor the displacement of landslides for real-time prediction. The FDMS can be quickly deployed compared to the existing system. This system also has high robustness due to the usage of the ad-hoc technique. The principle of this method is to extract the precursory features of the landslide from the surface displacement data obtained by the FDMS and, then, to train the KF-FFT-SVM model to make a prediction based on these precursory features. We applied this fast monitoring and real-time early warning system to the Baige landslide, Tibet, China. The results showed that the KF-FFT-SVM model was able to provide real-time early warning for the Baige landslide with high accuracy.
Highlights
Landslide hazard is one of the most common geological hazards in the natural world
Different instruments are used in single landslide early warning systems (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]
The real-time prediction method based on the Kalman filtering (KF)-fast Fourier transform (FFT)-support vector machine (SVM) model makes landslide predictions according to the principle that the mechanical vibrations of the landslide slope failure are recorded in the displacement data
Summary
Landslide hazard is one of the most common geological hazards in the natural world. They are directly affected by human engineering activities. 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 results demonstrated that the KM-FFT-SVM model can make a real-time precursor predication with high accuracy and good practicability
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