## 6 Conclusions  
Our study on predicting river discharge using deep learning algorithms is essential for proactive water resource planning and enhances our understanding of river behavior. Using four distinct deep learning algorithms across eight stations in the Niger Basin, we were able to identify and predict low extremes, defined as values below the 10 th percentile and annual minimum. This is crucial for meteorological and hydrological drought forecast, safeguarding agricultural irrigation, aquaculture, water transportation, and hydropower generation. Additionally, our investigation into high extreme flows provides important information for anticipating and managing flood events. Extreme scenarios can lead to adverse consequences, including disease outbreaks, ecological degradation from bank overflow, and damage to vital infrastructures. The proficient performance of these algorithms yielded reliable predictions and established their suitability for the unique challenges presented by the region. Ultimately, our study not only advances our comprehension of river dynamics but also equips decision-makers with tools to address the complexities associated with low and high extreme flows in the Niger Basin. Secondly, this study has shown the possibility of the use of deep learning algorithms in river flow (low and high) prediction within the River Niger basin. Even though this prediction cannot be made possible on a long-term basis due to the chaotic nature of the river system, deep learning algorithms can still help in short-term predictions. Across the stations studied, the study affirmed that no one-size-fits-all model can be used across the stations within the study area. The performances of various models were specific to some stations and peculiar for some flow levels. In essence, river flow prediction within the River Niger system is station-specific and should thus be studied as such. Peculiarities across each station become evident based on the station's location in terms of distance from the coast and nearness to the Sahel. In conclusion, this exhaustive examination of temporal variations in annual river discharge, conducted across diverse stations within the River Niger basin, furnishes invaluable insights into the dynamic compartment of this pivotal hydrological system. The temporal scope of the study spans from 1950 to 1990, elucidating discernible trends in minimum, mean, and maximum discharge values, thereby engendering implications for water resource management within the region. The observed diminishing trajectory in minimum discharge values, notably post-1960, posits a formidable challenge to water availability during the dry season, with profound repercussions for critical sectors such as hydro-power generation, fish farming, and dry season irrigation-fed agriculture. The multifaceted nature of these trends finds its roots in various contributory elements, encompassing alterations in land use, dam infrastructure development, teleconnections, and drought occurrences. This multifarious array of factors underscores the intricate responsiveness of the river system to an amalgamation of driving forces. Introducing a predictive dimension to the analysis, the deployment of deep learning algorithms (DLAs) for monthly river discharge prediction elucidates notable distinctions in performance across locations, with Long Short-Term Memory (LSTM) emerging as the preeminent model. However, it is imperative to acknowledge the location-specific variations in DLA efficacy, accentuating the necessity for bespoke methodologies tailored to the distinctive attributes of individual basins. The inherently chaotic nature of time series data, coupled with the influence of extraneous drivers, imposes limitations on the protracted predictive capabilities of DLAs--a pivotal consideration in the context of judicious water resource planning.