# Landslide Prediction with Model Switching +
## 3 Model Switched Landslide Prediction System  
A total solution for landslide prediction with early warnings is proposed in this work. The design of the proposed Model Switched Landslide Prediction System (MoSLaPS) is shown in Figure 1. It consists of two parts; namely, Physical Entities and Computation Elements. In the Physical Entities, environmental data, such as rainfall, soil moisture, and slope, are collected by sensor nodes. Coordinator nodes integrate the sensed data and transmit them to the Computation Elements through Zigbee transmitters. The Computation Elements consists of a SHALSTAB Model, a Switch-based Prediction Model, and an Accurate Early Warning System, as described in the following.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Stable Region** & **FS**\(\geq\) 1.3 \\ \hline
**Marginally Stable Region** & 1.3 \(>\) **FS**\(\geq\) 1 \\ \hline
**Actively Unstable Region** & **FS**\(<\)1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of Factor of Safety (FS) Levels.  
* SHALSTAB Model The SHALSTAB model takes the sensed environmental data, including rainfall, soil moisture, and slope, from the Physical Entities and evaluates the Factor of Safety, using Equation (1). Over time, the FS values are recorded as \(FS_{actual}=\{FS_{0},FS_{1},...,FS_{1}\}\), which is the input to the Accurate Early Warning System to predict the occurrence of landslides.
* Switch-based Prediction Model From historical environmental data, the proposed system consists of two prediction models to learn two different data patterns; namely, the stable pattern and the unstable pattern. To switch between the different prediction models, a neural network classifier is designed to predict the future class. The Switch-based Prediction Model can improve the prediction accuracy when the neural network classifier switches the prediction models accurately. The detailed technique is described in Section 3.3.
* Accurate Early Warning System The accurate early warning system consists of a data analysis server and alert services. The data analysis server uses the above-described switch-based prediction model to predict landslides. The input data, \(FS_{actual}\), is used to predict future FS values, denoted as \(FS_{predict}=\{FS_{t+1},FS_{t+2},...,FS_{t+n}\}\). For each predicted FS value, there is a difference between the predicted FS and the actual FS calculated using Equation (1). This difference is called prediction error. The data analysis server will assess the applicability of the prediction model, according to the trend of prediction errors. If the error exceeds a pre-defined threshold, it means the prediction model is not suitable for the environment at that time and the predicted results have large prediction errors. Thus, based on the error measurement and a given error tolerance threshold, the prediction model is re-trained. The entire process will be described in Section 4. If a predicted FS value, \(FS_{t+k}\), is smaller than 1, then it is estimated that a landslide will occur after \(k\) time slots [9]. Thus, alert services can send an alert in advance.  
The details of the prediction models, model switching, and early warning system will be introduced in the rest of this section.  
Figure 1: Model switched landslide prediction system architecture.