Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets.

Natural risks encompass a range of disasters and dangers, requiring comprehensive management through assessments, anticipations, and advanced alert systems. Our specific focus is on landslides in challenging terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum approach, integrating qualitative parameters. Despite the challenges of the rugged terrain in Northern Algeria, it paradoxically hosts a dense population attracted by valuable hydro-agricultural resources. Our research is dedicated to studying landslide risks in these areas, particularly in the Mila region, with the aim of constructing a statistical model integrating considerations of both danger and vulnerability. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. The result is a valuable tool for effective risk management, strengthening safety and resilience against natural disasters. By understanding the intricate interplay of geological, environmental, and social elements, our research can contribute to the development of targeted interventions, fostering a more resilient community in the face of natural adversities.

The aim of this research is the modelling and mapping of flood risk zones in the M’zab valley of Ghardaia city by combining the hazard map and the flood vulnerability map. The undertaken research utilizes the hydrologic engineering centre-hydrologic modelling system as the modelling tool to simulate the runoff in the watershed and hydrologic engineering centre – river analysis system to model the water levels of different river locations based on steady flow analysis and to define flood-prone areas. The flood risk map allows the identification of five classes ranging from a low to a very high risk of flooding. Thus, the low to very low-risk areas extend over roughly 70.58% of the total surface area of Ghardaia city. These areas, which are located in the southern part of the alluvial plain, are considered as low vulnerability regions, while areas with high-risk cover more than 20% of the city.

Assessing the hazards associated with aeolian geomorphological processes requires a fundamental understanding of their spatial distribution. These phenomena often have detrimental impacts on the environment, economy, and society. This problem is prevalent in the Algerian steppe, encompassing the Zahrez, Chergui, and Hodna regions. This study proposes a research method for developing more accurate and simpler indices to evaluate the extent and directionality of sand migration. Specifically, it examines surface characteristics, such as altitude, slope, and slope exposure. However, some tools used for spatial modeling of wind dynamics necessitate corrections to account for the effects of topography and surface features on wind, which for this study are implemented using spatial techniques. The results are incorporated into the model developed by Fryberger, which requires wind data and a Digital Surface Model (DSM) to estimate the factors included in this model. The findings indicate that the average potential quantity of sand movement is 64 t m-1 yr-1 over the entire study area, with 37.3% of the region experiencing severe deflation of 140 t m-1 yr-1. This result can be utilized to enhance the understanding of the direction and magnitude of sand movement in any region.