Abstract
Landslide is one of the most harmful geological disasters in the world. In order to effectively warn the landslide, Kalman filter is used to smooth the real-time Kinematic (RTK) positioning information of each monitoring point, remove outliers, improve monitoring accuracy, and extract information such as effective displacement. The attitude computation algorithm is used to solve the real-time attitude of the monitoring target, and the attitude prediction is realized which based on the deformation prediction information. Use the extended Kalman filter to realize the fusion of displacement deformation, velocity and acceleration data at multiple sites in the monitoring network, and to achieve the optimal estimation of comprehensive displacement. Use the MGM (1,1) gray model algorithm to realize the deformation displacement prediction. Use the comprehensive information amount to judge the landslide deformation grade. The early-warning algorithm is simple, easy to implement and practical, and can meet the actual requirements of landslide deformation early-warning.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
