# A 3D Two-Phase Landslide Dynamical Model on GIS Platform
## 4 Results  
To validate the feasibility of the three-dimensional model, we processed the generated grid information using Python to obtain the visualization of the three-dimensional model \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Landslide Body** & **Air** \\ \hline Mass density & 2150 kg/m\({}^{3}\) & \multicolumn{2}{c}{1 kg/m\({}^{3}\)} \\ Kinematic viscosity & 0.0465 m\({}^{2}\)/s & \(1.48\times 10^{-5}\) m\({}^{2}\)/s \\ Surface tension & & 0.7 N/m & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mechanical parameters of the Yigong landslide in the OpenFOAM. Figure 4: The three-dimensional geometric models of the landslide and landslide body with shape definitions. results on a two-dimensional plane. Specifically, within Paraview, we extracted pertinent grid data, including cell centroid coordinates, cell volume, and volume fraction of grid cells. Subsequently, we employed a Python script to perform a two-dimensional transformation and visualization of the three-dimensional model results as per Equation (17). Ultimately, we present the results of the three-dimensional landslide model on the GIS platform. The states of 20 s, 40 s, and 60 s using our three-dimensional model are given in Figure 5. Specifically, within the initial 20 s of motion, the landslide body was influenced by internal lateral pressure, and diffusion dominated the landslide's behavior, with lateral pressure being the primary driving force. Between 20 s and 40 s, the landslide body was primarily influenced by gravitational forces entering the channel, where gravity became the main driving force during this stage. Ultimately, the landslide body reached the foothill at 60 s. Throughout the entire motion process, the simulation results of the three-dimensional model exhibited greater dispersion, with a decreasing trend in the maximum accumulation thickness of the landslide body. This can be attributed to the influence of numerical oscillations and numerical diffusion. Meanwhile, regarding the distribution of the landslide deposit thickness, typically, regions with greater thickness should exhibit more concentration. However, influenced by the grid partitioning of the three-dimensional model, the distribution of the landslide deposit in the three-dimensional model appears relatively divergent, deviating somewhat from the actual scenario (Figure 5A,B). Nevertheless, the overall distribution range remains essentially consistent. According to the results of previous studies, [PERSON] simulated the Yigong landslide, which reached the mouth of the valley between 60 and 90 s [49]. [PERSON], employing the Eulerian method and depth-averaged SPH, achieved arrival at the mouth of the valley between 50 and 100 s [38]. Compared to these studies, the three-dimensional landslide dynamic model developed in this research exhibits a faster motion speed. The landslide reaches the mouth of the valley at around 60 s, a result attributed to the interFoam solver employed in our model. This solver treats all internal friction within the landslide as fluid friction, as opposed to solid friction such as Coulomb friction. Fluid motion resistance is smaller compared to solid friction, resulting in higher motion speed. This discrepancy is acknowledged as a limitation of our model. Figure 6 illustrates the variation in the deposit thickness of landslide deposits along profiles AB and CD. In profile AB, which extends along the channel direction, landslides are predominantly concentrated in the central portion of the channel. The thickness exhibits a trend of decreasing on both sides before increasing and then decreasing again. On the other hand, profile CD, oriented perpendicular to the channel direction, reveals that the thickness of landslide deposits increases as it approaches the central part of the channel.