This study focus on maps soil loss in the Zou catchment in central Benin to help protect farmland. Using satellite images, rainfall records and soil data, we identified areas where soil lost is most at risk. Most of the basin shows low soil loss risk, while small zones on steep slopes are more exposed. The results show the important for better land care and future work with higher-resolution satellite data.

This study aims to improve river flow prediction in a region where hydrological data are limited, which is essential for water management and flood preparedness. We combined a traditional rainfall–runoff model with data-driven learning methods to correct systematic simulation errors. Results show that the combined approach predicts river flow more accurately than the traditional model alone. These findings highlight a practical way to improve water resource planning in data-limited regions.