Disaster process and multisource information monitoring and warning method for rainfall-triggered landslide: a case study in the southeastern coastal area of China

Rainfall-triggered landslide are a typical geological hazard in the southeastern coastal area of China. The disaster process of rainfall-triggered landslide is investigated by field monitoring, model tests, three-dimensional reconstruction and discrete element numerical simulation. Intelligent monitoring and warning technology for rainfall-triggered landslide on the basis of multisource information is proposed. The results show that confining pressure has an effect on the disaster process of rainfall-triggered landslide. The failure scale and trigger time under confining pressure are smaller than those without confining pressure, which is more suitable for determining the failure characteristics of rainfall-triggered landslide. All-weather monitoring of landslides is carried out by collinear triocular visual equipment. Feature matching, point reprojection and point cloud generation are carried out by automatically extracting image depth information. A three-dimensional model of the prototype slope is constructed to identify its danger zone. A three-dimensional numerical model of a slope is established via the discrete element method on the basis of three-dimensional reconstruction technology. By analysing the variation characteristics of the displacement and soil pressure of a slope under rainfall, a two-variable early-warning technology of displacement–soil pressure is proposed, which integrates multiple early-warning technologies. The instability mechanism of landslides under extreme rainfall is investigated. Moreover, a multisource intelligent monitoring and warning system for rainfall-triggered landslide is developed by integrating information from model tests, three-dimensional reconstructions, numerical simulations, and field monitoring. The visualization and intelligent prediction of rainfall landslide disaster situations are realized. This work can provide a reference for monitoring and warning of rainfall-triggered landslide.