## 5 Results and Discussion  
The temporal variations in the annual characteristics of river discharge at different stations are presented in Figure 2. The minimum discharge measures (minimum and 10% percentile) were found to have a decreasing trend after 1960 at all the stations. The decreasing trend persisted into the 1990s at Diola, Douna, Dire, and Niamey. Kirango and Ansongo showed spikes in minimum discharge values around the 1980s. However, locations such as Koulikoro and Mopti were found to begin an increasing trend around the 1990s. The central tendency measures of river discharge (mean and 50% percentile) also showed a decreasing trend across all the stations since 1950 (Figure 2). The median value (50% percentile) was found to decrease to near-zero values in Diola and Douna. A recovery in the mean value of river discharge could be seen in the Ansongo station after 1980. Extreme values (maximum and 90% percentile) were also considered in this study. Both indices also showed decreasing trends at all stations; however, a slight recovery could be observed after 1980 in some locations. The decreasing trend in annual minimum, mean, and maximum values of river discharge has implications for water resources in the region. The decreasing trend in minimum values suggests that water availability during the dry season will be very low. Hence, energy production through hydro-power, fish farming, and dry season irrigation-fed farming will be affected [42; 43]. Broadly, the decrease in minimum discharge can be attributed to factors such as land use [44; 45; 46], construction of dams that regulate water flow [11; 46; 47], teleconnections [10; 48], and drought [49; 50]. Specifically, the spikes observed in Kirango and Ansongo after 1980 can be attributed to the Sahelian paradox, Sahelian recovery, and the accompanying change in land use-land cover within the region [51; 52; 53]. Despite the already established rainfall decrease post-1968, Sahelian stations have continued to experience increased runoff conditions promoted as a result of soil clearing and local soil crustinginduced by demographic growth [54; 55; 56; 57]. These studies and some others have shown the variability in river discharge are a result of various factors aforementioned. This could in no small way affect the services expected from the river system. Four MLAs were deployed for the prediction of monthly river discharge time series at the locations. The predicted data were compared with the original data (Figure 4) and the associated error analysis is presented in Table 1. In Koulikoro, the MLAs were able to replicate the periodic nature of river discharge but not the magnitude. LSTM was the best-performing model with an RMSE of 1044 m\({}^{3}\)/s, while TCN was the worst-performing model in Koulikoro with an RMSE of 1468 m\({}^{3}\)/s. However, TCN was found to be the best-performing machine learning algorithm in predicting river discharge at Diola with an RMSE of 174 m\({}^{3}\)/s. The TCN model simulates the periodic nature of river discharge with an increasing trend. With an RMSE value of 220 m\({}^{3}\)/s, GRU was found to be the worst-performing model in Diola. In Niamey, Kirango, Dire, and Mopti, the LSTM was found to have the lowest RMSE while RNN and TCN were the best-performing models in Douna and Ansongo, respectively. In Douna, the LSTM model failed after a few steps of prediction. All models, except TCN, did not capture the low river discharge values in Ansongo. The performance of the GRU model was the worst in all locations except Kirango. Generally, the performance of LSTM was found to be the best among the four models. The low performance of these models in the long-term prediction of monthly river discharge is attributed to the chaotic nature of the time series and the influence of external drivers [10; 11]. In their study, [PERSON] et al. [11] showed that the prediction of river discharge along the River Niger has a short prediction horizon due to the chaotic nature of the data.