# Landslide Prediction with Model Switching +
## 2 Related Work  
Landslide prediction methods can be classified into three types: Image analysis, machine learning, and mathematical evaluation models. Table 1 shows a comparison among these types of methods. First, image analysis uses Geographic Information Systems (GIS), which can collect, store, manage, and analyze geographical data. By analyzing disaster data, such as history of landslides and data on land development for agriculture, the risk of landslides can be predicted. The probability of landslides is variable, as it is based on the number of layers of data used for analysis. Second, machine learning techniques, such as Bayesian networks [2], neural networks [3], or genetic algorithm [4], use computational intelligence to calculate the probability of landslides. These methods incorporate different factors that might cause landslides to evaluate the probability of landslide occurrence. They are not real-time, because they require huge computational times for prediction. Finally, mathematical evaluation models use a single evaluate equation, such as Factor of Safety (FS) [5]. A hazard model is combined with the physical concepts of mechanics and hydrographic data for the stability of slopes. It is easy for simulation and fits a wide range of environments, but it is difficult to obtain the whole hydrographic data as groundwater elevation is difficult to measure. Landslides occur when the down-slope shear stress is large. As shown in Equation (1) [5], the Factor of Safety (FS) refers to the stability of the soils. It takes physical properties, including rainfall, slope, and soil properties, into consideration. It can easily predict landslides with the trend of each parameter. Therefore, to predict landslides, a FS equation is matched with these attributes. Three regions of the FS value, based on the SHALSTAB model [6], are defined to distinguish between the dangerous levels of a slope, as shown in Table 2. The Stable Region is classified as the stable class and the Marginally Stable and Actively Unstable Regions are classified as the unstable class. Based on the classes, different training samples are used to train multiple neural network predictors. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Types** & **Methods** & **Advantages** & **Accuracies** \\ \hline Image Analysis & Geographical & Suitable for & Accuracy based \\  & Information System [4] & Large Area & on number of layers \\ Machine Learning & Bayesian Network [2] & Simple Network & 75\% \\  & Neural Network [3] & Simple Network & 67\% \\  & Genetic Algorithm [4] & Optimal Solution & 90\% \\ Mathematical Evaluation & SHALSTAB\({}^{*}\)[6] & High Accuracy & >90\% \\ \hline \hline \end{tabular}  
\({}^{*}\)Shallow Landsliding Stability Model.  
\end{table} 
Table 1: Comparison of Landslide Detection/Prediction Methods. \[FS=\frac{C+(1-\frac{R}{T}\frac{\alpha}{sin\theta}\frac{\rho_{\varpi}}{\rho_{s}}) \rho_{s}gZcos^{2}\theta tan\phi}{\rho_{s}gZcos\theta sin\theta}, \tag{1}\]  
with  
* C: The effective coefficient (kPa);
* \(R\): The rainfall intensity (mm/hr);
* \(T\): The soil transmissivity (mm/hr);
* \(Z\): The soil depth (m);
* \(\rho_{w}\): The density of water (kg/m\({}^{2}\));
* \(\rho_{s}\): The density of soil (kg/m\({}^{2}\));
* \(\phi\): The internal friction angle of the slope material (degree);
* \(\theta\): The slope gradient (degree); and
* \(\alpha\): The specific contributing area [5]. Of particular mention is the work done by [PERSON] et al. [7; 8] on landslide displacement prediction using Prediction Intervals (PIs) and an ANN switched prediction method. The authors employed K-means clustering for dividing the landslide data into two classes; namely, a majority class (stationary points) and a minority class (mutational points). Then, a weighted Extreme Learning Machine (ELM) classifier was used to construct the switch rules. Finally, bootstrap and kernel-based ELMs were applied to construct the PIs. This work was concerned with how the displacements are predicted accurately and early.