###### Abstract
In conclusion, what is proposed is a working method that aims to evaluate the landslide risk induced by glacier melting at a regional scale, moving in a different direction from some existing studies based on the point-specific analysis of single glaciers or landslides and/or a more sectorial perspective [28; 29]. It is more related to the development of a methodology for predicting landslide hazards at a regional scale [30; 31]. The effective prediction of landslide hazards at a regional scale requires methodologies that integrate various data sources and analytical techniques. Several approaches have been developed to assess and predict landslide susceptibility, hazard, and risk. Statistical and heuristic models rely on historical landslide data and empirical relationships between landslide occurrences and contributing factors. Methods such as weights-of-evidence modeling and logistic regression have been widely used. [PERSON] et al. (2005) demonstrated the application of statistical models in mapping landslide susceptibility, highlighting the importance of comprehensive past landslide databases [32]. Deterministic models are based on the physical principles governing slope stability and often require detailed geotechnical and hydrological data. Examples include the infinite slope model and the limit equilibrium method. [PERSON] and [PERSON] (1994) developed a physically based model to predict shallow landsl by integrating topographic, soil, and hydrological data [33]. GIS is a powerful tool for landslide hazard assessment, allowing the integration and analysis of spatial data. [PERSON] et al. (2008) discussed the use of GIS for landslide susceptibility mapping, emphasizing its capability to handle large datasets and perform spatial analysis [34]. Remote sensing technologies, such as satellite imagery and LiDAR, provide valuable data for landslide detection and monitoring. The integration of remote sensing data with GIS has improved the accuracy of landslide hazard maps. [PERSON] et al. (2011) reviewed the application of remote sensing in landslide hazard assessment, noting its effectiveness in detecting terrain changes over time [35]. Recent advancements in machine learning have opened new possibilities for landslide hazard prediction. Algorithms such as random forests, support vector machines, and neural networks can analyze complex datasets to identify patterns and predict landslide occurrences. [PERSON] et al. (2018) demonstrated the application of machine learning techniques in regional landslide susceptibility mapping, showing improved prediction accuracy compared to traditional methods [36]. Probabilistic models assess landslide hazards by estimating the likelihood of occurrences based on statistical analyses of historical data and contributing factors. These models often incorporate uncertainty and variability in their input parameters. [PERSON] et al. (2014) discussed the utility of probabilistic models for landslide hazard assessment, emphasizing their ability to provide robust risk evaluation [37]. In this work, we are presenting a deterministic model based on a GIS approach with the following goals:* Assess the impacts of climate change on mountain areas, particularly due to glacier melting, reduced snow accumulation, and permafrost loss;
* Investigate the effects of cryosphere changes on slope stability and risks associated with flora, fauna, and local populations;
* Develop a GIS-based, multi-criteria approach for assessing landslide and slope stability risks related to rapid glacier melting, specifically in the Valle d'Aosta region in Italy;
* Identify hazardous areas impacted by glacier melting as a foundation for developing effective risk reduction strategies;
* Provide spatial planning tools to assist local authorities in adapting to climate change in Alpine environments.