## 5 Results and Discussion
For some other studies, possibilities in the use of machine learning algorithms to predict river discharge have been investigated [12; 15]. Our results differ from other studies that showed the best-performing model as multilayer perceptron when compared with DT, RF, GB, RNN-LSTM, and CNN-LSTM [15]. Invariably, the use of MLAs is effective in the prediction of flow within ungauged basins [12]. The ability to predict the annual river discharge statistics using machine learning algorithms was investigated (Figures 2-10). Table 2 shows the performance statistics of the machine learning models at the different stations. As shown in Figure 2, both LSTM and TCN models underestimated the annual minimum river discharge at Koulikoro station. RNN provides better tracking of the annual minimum discharge at the location than the GRU model. Hence, RNN was found to be the best model with an RMSE value of 30 m\({}^{3}\)/s while LSTM has the worst performance with an RMSE value of 81 m\({}^{3}\)/s. At Diola, all the models could not adequately predict the annual minimum discharge due to the zero values. However, LSTM, although providing negative predictions, has the best performance with an RMSE value of 0 m\({}^{3}\)/s closely followed by the GRU model with an RMSE value of 0.18 m\({}^{3}\)/s. The RMSE values of the Kirango models were 295, 908, 106, and 720 m\({}^{3}\)/s for RNN, LSTM, TCN, and GRU, respectively. There was significant overestimation in the LSTM and GRU models after a few timesteps of prediction. The inability of TCN to adequately capture high values contributed to the high RMSE values obtained. The annual minimum river discharge was largely zero in Douna; thus, the models could not sufficiently predict the values. LSTM estimated negative values for the prediction period, while RNN and TCN estimated negative values after a few timesteps. Both RNN and TCN recorded identical performances, which is attributed to their ability to predict the trend and early \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Model** & **Koulikoro** & **Diolla** & **Kirango** & **Douna** & **Mopti** & **Dire** & **Ansongo** & **Niamey** \\ \hline RNN & 1241 & 200 & 1315 & 193 & 814 & 421 & 478 & 454 \\ LSTM & 1044 & 211 & 953 & 239 & 517 & 412 & 412 & 280 \\ TCN & 1468 & 174 & 1052 & 245 & 621 & 445 & 327 & 291 \\ GRU & 1299 & 220 & 1171 & 689 & 948 & 632 & 674 & 547 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Root mean square error (m\({}^{3}\)/s) from different machine learning predictions of monthly river discharge data. timesteps of prediction. The LSTM model was found to have the best performance in predicting the annual minimum discharge at Mopti, Dire, and Ansongo, while TCN models had the best performance at Niamey. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Statistics** & **Model** & **Koulikoro** & **Dioila** & **Kirango** & **Downa** & **Mopti** & **Dire** & **Ansongo** & **Niamey** \\ \hline \multirow{3}{*}{Minimum} & RNN & 30 & 4 & 295 & 1 & 32 & 21 & 492 & 65 \\  & LSTM & 81 & 0 & 908 & 75 & 22 & 15 & 407 & 150 \\  & TCN & 61 & 2 & 106 & 1 & 35 & 251 & 410 & 17 \\  & GRU & 46 &  
Figure 5: Prediction of minimum annual discharge using different machine learning algorithms at the study locations. Figure 6: Prediction of 10% percentile annual discharge using different machine learning algorithms at the study locations. Figure 8: Prediction of mean annual discharge using different machine learning algorithms at the study locations. Figure 7: Prediction of 50% percentile annual discharge using different machine learning algorithms at the study locations. The 10 th percentile of the annual river discharge, a measure of minimum extreme, indicates low river discharge during the year. It is the minimum value below which we have extreme minimum discharge. This is an important measure whose knowledge will help in planning water resources within a region.