## 1 Introduction
There are 84,441 industries and service providers in areas categorized as high and very high landslide hazards, employing over 220,000 individuals at risk. Moreover, 642,979 local business units (13.4%) face potential flooding danger in medium-hazard scenarios. These figures highlight the broader implications of risk and the urgent need for comprehensive risk management strategies. * Cultural Heritage: There are 12,533 sites at potential risk of landslides in high- and very high-hazard areas, totaling 38,153 units, including those in less dangerous zones. In medium-hazard scenarios, 33,887 monuments are at risk of flooding, which increases to 49,903 in low probability or extreme events. The report indicates a significant increase in risk areas compared to the previous assessment. Specifically, there was a 3.8% rise in areas classified as high and very high landslide hazards and an 18.9% increase in areas with medium hydraulic hazards. This underscores the growing threat and the need for proactive measures. Furthermore, the report notes that 93.9% of Italian municipalities are at risk of landslides, floods, and coastal erosion. This study aims to develop advanced methodologies for predicting landslide risk susceptibility. The primary objectives include creating a system that can predict the landslide susceptibility index in potentially a risk area. This system will use specific input data to respond effectively to extreme weather events like heavy rainfall. Additionally, we aim to implement a neural network that can generate the desired outcomes by representing the terrain using cellular automata. To provide a clear and organized view of our study, we have structured the article into several sections, each contributing to a comprehensive understanding of the research. Below is an overview of the article's structure. The article begins with an overview of landslides, describing the different types, their causes, and the importance of reliable tools for risk prediction. We underscore the crucial role of institutions such as the IRPI and ISPRA in managing landslides. Next, we present real testmonies that illustrate the complexity of landslide phenomena and the solutions adopted to mitigate their effects. These testmonials create a context for the research and highlight the importance of an integrated approach to landslide risk management. The \"Materials and Methods\" section provides a detailed description of the model and methodology used in this study. It explains how cellular automata represent the terrain and how the random forest model is implemented to analyze and learn from weather patterns. Combining these tools allows for the creation of a landslide susceptibility prediction system with high spatial accuracy. The description of the study area offers a morphological, geological, and lithological overview of Prunella, a small village known for its numerous historical landslides. This description is essential for contextualizing the research results and understanding the specific characteristics of the territory analyzed. The Results section presents the elements characterizing the study model and the outcomes obtained. It includes comparing the proposed model's performance with another predictive model, highlighting the improvements achieved. The Discussion outlines the differences between the suggested model and those commonly used. We acknowledge the limitations of the proposed solution and discuss potential future implementations. This section critically reflects on the results and their practical implications for research. Finally, the Conclusions summarize the key elements that define the model and outline its potential applications. We emphasize the importance of an integrated approach in landslide risk prediction and management, highlighting how the proposed model can serve as a valuable tool for local authorities in territorial planning and emergency management.