Landslides are among the most relevant and potentially damaging natural risks, causing material and human losses. The department of Aube in France is well known for several major landslide occurrences. This study focuses on the assessment of Landslide Susceptibility (LS) using the Frequency Ratio (FR) as a statistical method, the Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) method, and Random Forest (RF) and k-Nearest Neighbor (kNN) as machine learning methods in the Aube department, northeast of France. Subsequently, the thematic layers of eight landslide causative factors, including distance to hydrography, density of quarries, elevation, slope, lithology, distance to roads, distance to faults, and rainfall, were generated in the geographic information system (GIS) environment. The thematic layers were integrated and processed to map landslide susceptibility in the study area. On the other hand, an inventory of landslides was carried out based on the database created by the French Geological Survey (BRGM), where 157 landslide occurrences were selected, and then RF and kNN models were trained to generate landslide maps (LSMs) of the study area. The generated maps were assessed by using the Area Under the Receiver Operating Characteristic Curve (ROC AUC). Subsequently, the accuracy assessment of the FR model revealed more accurate results (AUC = 66.0%) than AHP, outperforming the latter by 6%, while machine learning models results showed that RF gave better results than kNN (<7.3%) with AUC = 95%. Following the analysis of LS mapping results, lithology, distance to the hydrographic network, distance to roads, and elevation were the four main factors controlling landslide susceptibility in the study area. Future mitigation and protection activities within the Aube department can benefit from the present study mapping results, implicating an optimized land management for decision-makers.

The Assaka watershed is one of the largest watersheds in the Guelmim region in southern Morocco. It is frequently exposed to the many flooding events that can be responsible for many costly human and material damages. This work illustrates a decision-making methodology based on Analytical...

Soil loss is a problem that contributes to land degradation in many countries and Morocco is no exception. Our study focuses on water erosion in Korifla, a sub-basin of the Bouregreg watershed in northern Morocco. The objective is to quantify erosion using the RUSLE method which is based on five...

In the last decade, Morocco has experienced increased population, urban expansion, and improper environmental management, leading to a significant rise in waste production. This situation has exacerbated waste landfill issues, particularly in coastal areas such as Kenitra province, in the...

Waste management is a crucial issue for environmental preservation, public health, and sustainable development, especially in rapidly growing regions such as kénitra province, Morocco. This study aims to assess the potential environmental impacts associated with the establishment of a landfill...