Nowadays, modelling groundwater potential zones (GWPZs) based on scientific principles and modern techniques is a major challenge for scientists around the world. This challenge is even greater in arid and semi-arid areas. Unmanned aerial vehicles (UAVs), geographic information systems (GISs), and multi-criteria decision making (MCDM) are modern techniques that have been applied in various fields, especially in groundwater exploration. This study attempts to apply a workflow for modelling the GWPZs using UAV technology, GIS, and MCDM in semi-arid areas. An aerial survey provided a high-resolution DEM of 4 cm. Six influencing factors, including elevation model, drainage density, lineament density, slope, flood zone, and topographic wetness index, were considered to delineate the GWPZs. Four classes of groundwater potential were identified, namely high (4.64%), moderate (23.74%), low (18.2%), and very low (53.42%). Three validation methods, namely borehole yield data, receiver operating characteristic area under the curve (ROC-AUC), and principal component analysis (PCA), were used and gave accuracies of 82.14%, 65.4%, and 72.49%, respectively. These validations indicate a satisfactory accuracy and justify the effectiveness of the approach. The mapping of GWPZs in semi-arid areas is very important for the availability and planning of water resources management and for sustainable development.

The construction of sustainable road and highway networks in the world, despite numerous feasibility, pre-feasibility and execution studies, are always confronted with the demands and vagaries of foreseeable and unforeseeable natural disasters. Studying cyclones, earthquakes, fracturing and landslide zones along roads is therefore a challenge for the sustainability of these infrastructures. In many countries around the world, the methods generally used for these studies are not only expensive and time-consuming, but also the results obtained are not always efficient. This work examines whether Landsat 8 (with a high cloud level) and SRTM data can be used in both equatorial and coastal Central Africa zones to produce relevant mapping, locating fracture and landslide zones, in order to contribute not only to a better road layout at lower cost and in a relatively short time, but also to a better prevention of geological disasters that may occur on its network. To this end, a map of the main road network was produced and validated with field data, as well as the maps of the main unstable slopes, faults and fractures zones intersecting the road or highway network. These approaches are useful for sustainable planning, management, monitoring and extension of roads worldwide, especially in Central Africa.