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The large demand for drinking water in Oumé Department in the Centre western of Côte d'Ivoire is supplied from groundwater sources. This study investigated the geochemical assessment of groundwater quality in Oumé Department by using a hydrochemical approach with graphical and self-organizing maps (SOM) neural network methods. It was carried out for identifying the hydrogeochemical processes related to groundwater quality, conducting a hydrochemical evaluation of the aquifer systems and delineating the various factors controlling the water chemistry and general suitability for drinking purposes. To reach these goals, groundwater was sampled from 91 locations. Results indicated that the groundwater sampled is acid (pH 4.27-7.10) and was weakly fairly mineralised with electrical conductivity values obtained in the range of 95-1071 µS•cm −1. All calcium (6.8-127.59 mg•L −1), magnesium (1.08-29.00 mg•L −1), sulphate (0-74.9 mg•L −1), chloride (1.2-89.8 mg•L −1), sodium (1.2-94.27 mg•L −1) and potassium (0.05-23.65 mg•L −1) concentrations and almost nitrate concentrations were within of the recent acceptability and healthbased of drinking-water guidelines set by World Health Organization (WHO). Moreover eleven sampling sites only have concentrations of iron above 0.3 mg•L −1 which can stain laundry and cause taste. In the water points, hydrochemical facies was calcium-bicarbonate (Ca-HCO3) type which generally shows less-polluted water quality. Based on pattern analysis, the interrelationships among the groundwater quality variables due to the contact with different geological formations on the basis of rocks basicity and acidity were extracted and interpreted.

The high populated and dense agricultural region of Divo-Oumé, in the southern part of Côte d’Ivoire, periodically faces drinking water scarcity and lack of reliable water resources management tools. This study presents a statistical and geostatistical analysis of lineaments network, conducted in Divo-Oumé region, in order to contribute to a better understanding of local Precambrian basement aquifer. Fractures network has been characterized through the analysis of the attributes of lineaments derived from conjoint Radarsat and Asar images. The average length of lineaments is 2.15 km and around this value, high dispersion is observed (CV = 133%). A total of 3,559 cross-points (CP) are identified and most of spacing values (84.3%) are less than 2 km. The statistical distribution of lineaments lengths, cross-points and spacing follow power law, highlighting the fractal nature of fractures network. Also, the variation coefficient (CV = 51%) of lineaments spacing and the characteristic exponent of power law in the case of lineament lengths (α = 2.55) would indicate respectively, fracturing process is intense in the region and fracture network reach a mature stage of development. The geostatistical analysis showed that the variograms of lineaments cumulative lengths (CL) and cross-points (CP) are structured. These variograms have the same behavior, reflecting the intrinsic character of CL and CP. However, the correlation ranges of CL are higher than those of CP. These results could be useful for understanding groundwater flow and the establishment of water resource management tools.