## 6 Discussion  
Various qualitative and quantitative methods can be applied for the assessment of landslide susceptibility [47]. Qualitative methods are inventory-based and knowledge-based methods. These subjective methods are rarely used now. By contrast, quantitative methods, which are data-driven, are very effective in predicting the occurrence of landslides in both spatial and temporal domains. These methods require detailed geological data in the field, which are difficult to collect for large areas. As a result, data-driven machine learning and soft computing methods are widely used for the assessment of landslide susceptibility. The adoption of soft computing techniques, such as neural networks and cellular automata, allows for more accurate modeling of landslide phenomena and improved predictions compared to traditional methods. Cellular automata (CA) are discrete and parallel computational models that simulate dynamical systems based on local interactions. Natural events, such as landslides, can be effectively modeled using this approach. In our model, cellular automata represent the terrain, where each cell corresponds to a portion of the territory. Each cell can assume different states depending on ground conditions and atmospheric events. The transition rules of the cellular automata are designed to simulate the evolution of soil conditions in terms of slope, soil composition, and atmospheric factors. Figure 14: Susceptibility map. The random forest model analyzes climate and terrain data. This machine learning algorithm is trained on a large dataset to identify patterns and correlations between atmospheric conditions and landslide triggers. It learns from historical data and continuously updates itself with new information, improving accuracy. When atmospheric data are collected in real time, they can be integrated into the cellular automata model to update the status of the cells according to current conditions. The random forest model then analyzes these updated data to calculate a susceptibility index for each cell, identifying areas at risk of landslides. The susceptibility index can be compared to predefined thresholds to determine whether an alarm should be issued. This approach allows for early warnings that can be incorporated into existing emergency protocols, enhancing response efforts to natural disasters. Therefore, while our system does not predict landslides in real time, it provides a real-time indication of ground susceptibility, enabling appropriate preventive measures to be taken. Combining cellular automata and random forests for landslide prediction represents an innovative approach. Cellular automata help model terrain's spatial and temporal dynamics, while random forest provides a robust analysis of climate and terrain data to identify complex patterns. This model offers high spatial accuracy, crucial for pinpointing vulnerable areas and implementing preventive measures. It is particularly beneficial in urban environments and regions with complex topographies. Additionally, the model is designed to continuously update with new climate and terrain data, enhancing its accuracy over time and allowing it to adapt to changes in environmental conditions [35]. The model can be integrated into existing emergency protocols, providing early warnings that enhance response efforts to natural disasters. This makes it a valuable tool for local land management and urban planning authorities. When compared to other methodologies reported in the literature, this model differs in several key aspects:  
Traditional landslide prediction models: These are based on complex differential equations that require significant simplifications and large amounts of detailed data. In contrast, our cellular automata approach utilizes simple rules to represent local interactions between cells, avoiding these complexities. GIS-based models: These models require high-quality data and can be expensive to implement.