## 4 Different Landslide Prediction Methods  
### Physically Based Models  
Physically based models have been widely used due to their excellent advanced data, potential and appropriateness for quantifying the impacts of certain factors leading to the initiation of landslides. Due to their capacity to mimic the physical processes governing the recurrence of landslides, the physically based approach may be useful for studying the resilience of shallow landslides [32; 33]. Slope resilience is evaluated physically by evaluating the forces operating on it with the use of a physical slope model, which may include an infinite slope method [34]. The infinite slope model is a simple but effective representation of a rainfall-induced landslide on a slip surface parallel to the ground slope. Due to its premise that landslides are infinitely long but have a minimum depth in relation to their width and length, this model is well-suited for the study of rainfall-induced landslides with planar slope failure [29; 35]. SINMAP utilizes pore water pressure from a topographic consistent hydrogeology model based on the infinite slope stability approach. SINMAP classifies terrain stability based on hydrological, topographic and soil properties [34]. The SINMAP model, which integrates hydrologic processes, geotechnical data, and terrain, is ideal for dealing with this issue. Because precipitation and groundwater circulation may affect soil moisture and produce flow convergence in catchment areas where landslides occur regularly, hydrological variables play a key role in causing shallow landslides [36]. \[FS=\frac{1}{X}\left(A+B+C\right)\]  
\[X=D\rho_{s}\mathcal{I}\sin\theta\cos\theta\]  
\[A=C_{r}\text{; }B\text{ }=C_{s}\]  
\[C=\cos 2[\rho_{s\mathcal{I}}(D-D_{w})+(\rho_{s\mathcal{I}}-\rho  
attributed to inadequate mapping scale or technique. MaxEnt and SINMAP have been used in this study to provide an innovative hybrid method for landslide susceptibility modeling. Figure 11 shows a hybrid model which combines SINMAP with Entropy (MaxEnt). The data collected (slope and catchment area) are obtained through a digital terrain analysis model (DEMs). These parameters may be changed and calibrated using an interactive visual process that modifies them in response to observed landslides. Across the globe, landslides are a major cause of death and property damage. Landslide hazard maps have been developed over the last several decades by a variety of academics using both descriptive and analytical methodologies [38]. DEM, geography, and other factors are used to investigate the interaction of landslides, as shown in Figure 12. Obtaining reliable findings from landslide research requires sufficient precise data from a broad variety of criteria SINMAP accounts for unknown parameters by constructing regular random variables with lower and upper bounds [39]. Figure 11: SINMAP combined with Entropy (MaxEnt) [37]. In terms of the probability density function between these boundaries, the parameters are assumed to vary randomly. By maintaining uniform parameter distributions across complex ranges, this model produces the stability index (SI), which is defined as the probability that a location is stable. To predict the possibility of shallow landslides occurring, dynamic pressure fluctuations due to rainfall and downward incursion are included in the TRIGRS method. The S software enables the combination of infinite slope stability calculations with a one-dimensional empirical method for pore-pressure filtration in a limited depth toposoil in response to time-varying precipitation [41; 42; 43]. While transitory models may improve the quality of susceptibility results by accounting for the transient impacts of changing rainfall on slope stability conditions, they often need a significant quantity of data [44].