Knowledge of information related to land use and land cover in a region is necessary for urbanization projects, sustainable development and natural risk management, particularly floods. The aim of this article is to explore the use of Artificial Intelligence techniques and the combination of multi-sensor images to map land use and land cover in the Marahoué region. To this end, the Deep Forest algorithm is used as the main classifier. Its construction required the use of three common classifiers Extreme Gradient Boosting (XGB), Random Forest (RF) and Extra Tree (ET). Three Deep Forest models (DF-XGB; DF-RF; DF-ET) were developed and optimized to guarantee optimum accuracy. These DF models were then compared with four (04) classifiers commonly used in land use studies (RF, XGB, CNN, CART). The results indicate that the DF-XGB model outperformed all conventional classifiers by over 96%, confirming the relevance of integrated approaches mobilizing multi-sensor data, spectral indices and advanced classifiers. The predominance of cultivated land, the regression of forest formations and the localized presence of wetlands identified by the DF-XGB model, reflect the ongoing dynamics of anthropization. This approach thus offers a powerful tool for environmental monitoring, sustainable community management and flood risk prevention in the Marahoué watershed.

Poitevin marsh is a typical example of anthropogenic activities influence on wetlands. One of the problems to be solved before considering preservation solutions, concerns the improvement of hydrogeological knowledge and water transfers between the marsh and Jurassic carbonate bedrock. This study aims to determine the hydrodynamic parameters of Quaternary and Upper Oxfordian aquifers in order to appreciate the water transfers between these aquifers and to better understand the hydrogeology of this area. Thus, pumping tests are carried out to determine the hydrodynamic parameters of the aquifers. The pumping tests interpretation carried out, allowed to highlight very low permeability of upper Oxfordian formations under Quaternary cover. As for the formations of Quaternary aquifer, obtained permeability coefficients are in the order of 10-7m/s to 10-6m/s with transmissivity values in the order of 10-6 to 10-5 m2/s and a storage coefficient in the order of 10-2. However, Quaternary formations are more permeable than Upper Oxfordian formations. On the time scale of the measurements, no hydraulic connection between Quaternary and Upper Oxfordian aquifers could be observed. The water transfers between these two aquifers would therefore be very limited.