Every year in Côte d’Ivoire, during the rainy season, several localities, including those within the Marahoué watershed, are subject to recurrent floods that cause loss of human lives, population displacement, and significant material damage. Identifying areas sensitive to this phenomenon is therefore crucial for the implementation of effective and sustainable preventive measures. This study thus aims to identify flood-prone zones in the Marahoué watershed by using current physical and hydrological characteristics derived from remote sensing observations, with the goal of supporting decision-makers in their sustainable development projects. The methodological approach employed begins with the delineation of the watershed into sub-basins, followed by the extraction of topographic, morphometric, hydrological, and environmental parameters associated with flooding, and the identification of sensitive areas through game theory algorithms (Borda and Condorcet). The results indicate that the downstream areas of the Marahoué watershed, particularly those located along the main river course, are highly sensitive to flooding. Conversely, upstream areas, which are generally situated at higher altitudes, were classified within the low to very low flood risk categories.

Streamflow data are very important in assessing the groundwater and surface water resources of a given region. In northern Côte d’Ivoire, particularly in the Bagoé region, although there are long series of rainfall data, streamflow data are still scarce. The few chronicles available are very short and incomplete. The aim of this study is to obtain a long flow chronicle for the period 1996-2016. It aims to estimate flows in the Bagoé River at the Kouto hydrometric station using neural networks. To this end, two neural models were developed to estimate variations in monthly flows of the Bagoé River from 1996 to 2016. The modeling was validated using the Nash criterion (%), the Pearson coefficient (R), the maximum flow ratio and the robustness criterion. The results showed that the validation criteria for these models are optimal. The Nash criterion is greater than 84% for both calibration and validation. The Pearson coefficient ranged from 92% to 96% in calibration and validation. The maximum flow ratio ranges from 93% to 118% in calibration and validation. The robustness criterion ranged from 2.91% to 7.62%. All these results reflect the good performance and stability of neural network-based models for estimating flows in the Bagoé river.

This study is entitled «Analysis of the rainfall aggressiveness on the soils of the N’Zi watershed. The objective is to analyze the importance of precipitation on soil erosion. To achieve the objective, daily, monthly and annual rainfall data on the N’zi watershed were collected over the period 1960 to 2019. The analysis of rainfall risks was possible for the index of erosivity of Arnoldus and Rango-Arnoldus rainfall, to graphical and spatial representations with R software and ArcGIS software. The results of the analysis of the interannual variability of precipitation show that the temporal aggressiveness generally declined during the 1970s. The rainfall aggressiveness indicates on a monthly scale a weak aggressiveness in general and the strongest would be seasonal at the level of the localities of the basin. It highlights three levels of rainfall aggressiveness on an annual scale: very aggressive aggressiveness (32%), less aggressive (48%) and more or less aggressive aggressiveness (20%), at the basin scale. Five (5) classes were distinguished; excess dominance (41%) qualified as high rainfall aggressiveness, dry period (30%) qualified as low rainfall aggressiveness, a minimum period (18%) qualified as very low rainfall aggressiveness, a maximum period (9%) qualified very high rainfall aggressiveness and a normal period (2%) qualified as average rainfall aggressiveness.