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.

The surface water resources in the region of Bonoua are subject to strong anthropogenic pressures created by the development and expansion of agricultural activities and also by the agro-industrial and domestic activities. This impacts the available quantities of these resources that fluctuate throughout the year. Similarly, their quality is thereby also changed. Regarding the quality of these resources how can occur at a given time of year? This concern led in this study to assess the overall quality of surface waters of Bonoua region during the rainy season. It is based on the exploitation of data from a water sampling campaign conducted in July 2014. The data were processed with the Evaluation System of Quality of Waters, referred to as "SEQ-Eau" for determining the overall water quality. The (ACPN) has also been applied to the data for the identification of phenomena causing water pollution. The results show that in general the surface waters in the region are Bonoua of "average quality" to 92.86% and "poor" at 7.14%. The responsible alteration of this deterioration in the quality of surface water is the "acidification" parameter, which contributes to 64%. Alterations "temperature" and "organic and oxidizable materials" expressed as COD and BOD 5 incidentally involved in this deterioration in the respective proportions of 14 % and 22 %. The phenomena causing water pollution is anthropogenic. They are mainly from discharges of domestic and industrial waste water and the use of chemical fertilizers in the plantations. This evaluation of the quality of surface water in the region of Bonoua is a first draft that should be complemented by other studies covering aspects such as pesticides and microbiology for better management.