## 2 Background
### Studies and Testimonies  
The literature contains various testimonies regarding the use of different forecasting models for landslide risk. In this section, we report the key results of some research works highlighting the methodologies used and the implications for environmental policies. These studies contribute to increasing our understanding of the challenges and needs of identifying areas subject to landslide hazards for the protection of the territory and the communities involved. [PERSON] et al. [18] introduced a method for accurately detecting infrasonic signals associated with debris movements during flows. This method employs a combined deep learning model that integrates the feature extraction capabilities of ResNet18 with the attention mechanism of the Vision Transformer. The system is then trained using this combined model, and experimental results demonstrate a recognition accuracy of up to 88.60%, affirming the model's effectiveness in predicting debris flow events. This study highlights the importance of integrating artificial intelligence techniques to improve landslide prediction, an approach we took in our study. [PERSON] et al. [19] conducted a comprehensive analysis of C-band synthetic aperture radar (SAR) data from European Space Agency (ESA) satellites (ERS-1/2, Envisat ASAR, and Sentinel-1) spanning the period from 1992 to 2020. They aimed to track spatial and temporal changes in surface deformations within the Brienz/Brinzauls landslide complex in the Canton of Graubunden, Switzerland. The authors utilized differential interferometry (InSAR), multitemporal stacking, and persistent scatterer interferometry (PSI) techniques across approximately 1000 SAR images. Additionally, they combined InSAR results with digital image correlation (DIC) on high-resolution digital terrain models (DTMs) to compute three-dimensional surface deformation fields. The accuracy of these results was validated using a network of Global Navigation Satellite System (GNSS) stations within the landslide complex field. This approach is similar to the one we have referred to in the Methodology section. [PERSON] et al. [20] proposed a novel method for identifying and monitoring landslides through image compression and a differential synthetic aperture radar interferometry (DS-InSAR) technique. This method, known as image compression DS-InSAR (ICDSI), applies principal component analysis (PCA) to compress synthetic aperture radar (SAR) images in the time domain. This innovative study supports the use of advanced remote sensing technologies but requires complex algorithms for data processing, and image quality can be affected by environmental conditions and terrain characteristics. In their study following the severe floods in Wanzhou, China, on 4 July 2023, [PERSON] et al. [21] investigated the impact of human activities on climate change and the increasing frequency of extreme weather events globally. They identified a growing relationship between these phenomena and global warming. Extreme rainfall, which often results in devastating urban flooding and mountainous landslides, is becoming more frequent and intense due to climate change. This context is relevant to our study, which aims to predict landslides in response to extreme weather events. [PERSON] [22] conducted a study analyzing early warning systems (EWSs) for landslides. These systems aim to protect assets and stakeholders by predicting potential failures and providing timely warnings. Their study evaluated four prediction methods using a comprehensive database of 75 historical failures. This study highlights the importance of developing early warning systems, a key goal of our model. [PERSON] et al. [23] conducted a study using Markov chain models and Monte Carlo simulations to model the probability distributions of landslide velocity over time. This innovative approach enables more robust and detailed probabilistic modeling. Previous studies often needed more accurate estimates due to insufficient consideration of stochastic variabilities.