## 5 Results and Discussion
Several factors including precipitation, land use, and teleconnection patterns can contribute to the 10 th percentile of annual river discharge [10]. Figure 6 shows the 10 th percentile river discharge predictions from the four machine learning algorithms. Among the machine learning algorithms, only TCN was able to capture both the trend and dynamics of the 10 th percentile river discharge at Koulikoro, thus becoming the best-performing model with an RMSE value of 40 m\({}^{3}\)/s. GRU grossly  
Figure 10: Prediction of maximum annual discharge using different machine learning algorithms at the study locations. Figure 9: Prediction of 90% percentile annual discharge using different machine learning algorithms at the study locations. underestimated the discharge at this location. The predictions by the models in Diola showed small errors in the range 1-7 m\({}^{3}\)/s. All the models, except RNN, overestimated the 10 th percentile of annual river discharge at Diola, however, to different degrees. RNN had the lowest RMSE value of 1 m\({}^{3}\)/s to emerge as the best model for the location's 10 th percentile of annual river discharge. The RNN model was also found to be the best model in predicting the 10 th percentile of annual river discharge at Douna, Mopti, and Dire, while the best-performing models at Kirango, Ansongo, and Niamey were TCN, LSTM, and TCN, respectively. In Dire, the RNN model was able to capture both the trend and fluctuations in the discharge data, while the TCN model predicted negative values. From the foregoing, as observed in this study, it is not uncommon for various models to outperform each other across different basins or locations as observed in this study. [PERSON] et al. [18] reported variations in the performance of different models with machine learning algorithms outperforming basic or traditional hydrological models. This is largely due to the data's inherent strengths, weaknesses, and peculiarity. The LSTM-RNN algorithm has been found to outperform the traditional RNN and Naive Bayes algorithms in predicting low flows [30]. Although GRU has identical performance to LSTM, it was found to be better in the short-term prediction of river flow [58]. The ability to predict the minimum and 10 th percentile annual discharge in a given year will help prepare the region for water scarcity and its compound effects. Farmers will be able to make irrigation plans and adjust their planting approach. Furthermore, predicting low flows aids fish spawning and catch in the basin of interest [59]. This will enable adequate mitigation plans such as pond farming and importation. Foreknowledge of the annual minimum discharge is also important for hydropower generation; it will give insight into the potentially available power during the year. In addition, it will help in planning for alternatives to river transportation during periods of low flows. Two measures of central tendencies were considered for the annual river discharge prediction: the 50 th percentile (Figure 7) and the mean value (Figure 8). The annual river discharge's 50 th percentile (Q50) represents the median value. The TCN model was the best model for Q50 (187 m\({}^{3}\)/s) prediction, while the GRU model performed best in predicting the mean value at Koulikoro. The worst performing models for both Q50 and mean value were found to be LSTM (375 m\({}^{3}\)/s) and RNN (678 m\({}^{3}\)/s). In Diola, the Q50 values showed decreasing values similar to the mean. However, the mean discharge value increased towards the end of the century. The models could not capture the decreasing trend in Q50; however, they captured the trend in mean value but not the reversal towards the end of the century. TCN was found to be the best-performing model in both Q50 with an RMSE of 44 m\({}^{3}\)/s and annual mean discharge value with an RMSE value of 31 m\({}^{3}\)/s. LSTM was the worst-performing model with RMSE values of 158 m\({}^{3}\)/s and 33 m\({}^{3}\)/s in Q50 and mean discharge, respectively.