## 5 Results and Discussion
A similar result was obtained in Kirango, with TCN and LSTM as the best- and worst-performing models, respectively, for Q50 and mean discharge. TCN and LSTM have identical performance metrics (18 m\({}^{3}\)/s) for Q50 in Douna. However, the two models could only replicate the trend and not the unique patterns in the discharge. RNN and GRU have similar performance in Douna with RMSE values of 78 m\({}^{3}\)/s and 79 m\({}^{3}\)/s respectively. LSTM performed poorly in Douna with an RMSE value of 97 m\({}^{3}\)/s. In Mopti, LSTM (179 m\({}^{3}\)/s) and RNN (357 m\({}^{3}\)/s) were the best- and worst-performing models, respectively, for Q50. However, the reverse was the case in the mean discharge value with RNN showing the best metric and LSTM the worst. The TCN model was observed to have the smallest RMSE values for Q50 in Dire and Ansongo, while the LSTM model had the best performance in both stations in mean discharge values. The dynamics of both median and mean discharge values in Niamey were best captured by the GRU model. This study's TCN model performed better than the other models in predicting Q50 values. TCN has also been better than LSTM in predicting extreme river discharge in the Yellow River [40]. The results obtained in this study were within the range 19.6-362 m\({}^{3}\)/s using GRU [20] and 8-83 m\({}^{3}\)/s using TCN [40], but higher than the range 2-6 m\({}^{3}\)/s using LSTM [19] and 0.137-0.174 m\({}^{3}\)/s using SVR [60]. It is worth noting that not much investigation has been performed regarding predicting river discharge using the TCN model. The median and mean discharge values give an insight into the river's average discharge during the year. A low mean value suggests that the discharge might be low, while a high value might imply a higher than normal discharge. Predicting the median and mean river discharge values in different rivers will give an understanding of potentially available water resources during the year. This will enable proper planning, mitigation, adaptation, and preparation. High river discharge extremes imply the availability of water resources. However, this causes uncertainty in water transportation and fish farming. Furthermore, high river discharge that overflows banks tends to destroy crops and that such as houses and bridges. The availability of high water volume which cannot be accommodated by a hydropower reservoir tends to be released, causing ecological damage to the surrounding environment. Machine learning prediction of the 90 th percentile (Q90) and annual mean discharge are shown in Figures 9 and 10, respectively. With RMSE values of 1355 m\({}^{3}\)/s and 1747 m\({}^{3}\)/s, the RNN and TCN models were the best- and worst-performing models in the prediction of Q90 in Koulikoro. However, the LSTM models gave the lowest RMSE value for the annual maximum river discharge in the same location. The GRU model was found to have the best performance for both Q90 and annual maximum discharge at Kirango and Ansongo. Similarly, TCN and RNN also showed superior performance for the two parameters in Douna and Dire, respectively. In Diola, the LSTM model outperforms the other models for Q90 but the TCN model performed best for the maximum river discharge. In Mopti, the TCN model has the best performance for Q90, but GRU has the best performance for the same quantity in Niamey. However, in both locations, the best-performing model for annual maximum river discharge was the LSTM model. [PERSON] et al. [61] proposed using the GRU model for river discharge prediction due to its better performance than the LSTM model. Among the various recurrent network variants, the LSTM model has been recommended for river discharge forecasting due to its accuracy [62]. It was further confirmed that the LSTM performs better in the presence of dams and reservoirs [63,64]. Prediction of a higher than average Q90 and annual maximum river discharge suggests flooding in the given year. This will enable adequate preparation to mitigate losses and maximize the potential in the discharge.