# Web3 DGIS-Based System for Reservoir Landslide Monitoring and Early Warning
## 4 Key Technologies
### Landslide Forecasting Model  
A landslide-forecasting model must determine the time that a landslide reaches its accelerated deformation-instability phase. The stability of a landslide is affected by internal factors (e.g., landslide shape, soil/rock mechanical parameters) and external factors (e.g., groundwater level, rainfall, and human construction work), which not only have different effects on the landslide, but are also constantly changing.  
At present, models for forecasting the time of landslide instability include the Saito Model, creep-spline Combined Model, Verhulst Model, the grey Model (1, 1) and so on [35; 36; 37; 38]. Each model describes the relationship between instability time and deformation values and velocity, as estimated by the surface conditions, and is able to predict when a landslide will enter the accelerating deformation-instability phase. However, for large areas, we have to set many landslides monitoring sites independently, so using a single forecasting model is not sufficient. In this study, three models were selected: a landslide deformation forecasting, non-linear dynamics model; a grey forecasting model that varies with different periods; and a multi-factor grey forecasting model. These models are respectively responsible for short-term forecasting of relatively unstable landslides, long-middle term forecasting of relatively stable landslides, and forecasting landslides easily affected by external factors. In detail:  
1. Non-linear dynamics model. By nature, landslides are a non-linear dynamic systems controlled by soil and rock conditions (e.g., topography, groundwater, rainfalls, and human engineering). The non-linear dynamics model has long been used [39; 40], and has been developed to reach a high level of accuracy in the short-term prediction of landslides. This study applied the non-linear dynamics model proposed by [PERSON] _et al._[19], which makes predictions based on three major factors: stress, landslide displacement, and groundwater.
2. Grey forecasting model. This model, proposed by [PERSON], is a method for forecasting systems with uncertain factors [38]. By collecting slices of incomplete information, this model was built to consider a long-term general description of a system's development. [PERSON] _et al._[41] used this method to monitor and forecast ground surface deformation, and since then it has been commonly used in landslide forecasting [42; 43; 44]. In this study, the grey model was combined with a time sequence analysis model, such that after amendment of the residual model in time sequence, it was able to forecast trends of uniform motion-accelerating motion, accelerating motion-uniform motion, slow change-uniform motion, and slow change-accelerating motion. Through extraction of the items, which shock periodically, the model can greatly offset influences from external factors.
3. Multi-factor grey forecasting model. This model, from the perspective of both internal and external factors that can affect landslide deformation, regards the whole landslide as the production of multiple affecting factors. The description of internal affecting factors are modeled after a tendency grey model that varies with different periods, while external affecting factors can be divided into several independent but related linear factors. By combining the model with internal and external factors, a multiple and combined model is constructed and can be used for grey forecasting [45]. The multi grey model considers both external and internal factors relating to deformation, and when these change, the deformation value can be estimated as a fixed quantity. The model is suitable for forecasting landslide deformation affected by external factors (e.g., rainfall and groundwater level).