## 4 Conclusions  
In this current study, the efficiency of ML models (long short-term memory, support vector regression, and extreme learning machine) and a coupled model combining physically based (HEC-HMS) and ML models were presented to predict the daily runoff in the Voshmgir basin, Golestan province, north of Iran. TRMM satellite data were used to simulate and forecast river runoff in the selected basin. For the simulation models, six years of data were used during the calibration and validation phases. Rainfall-runoff modeling was applied daily using the HEC-HMS, LSTM, SVR, ELM, HEC-HMS-LSTM, HEC-HMS-SVR, and HEC-HMS-ELM models. Data from the validated section of the HEC-HMS from 2005 to 2007 were applied to the coupled models. The findings revealed that, based on our assumption, TRMM satellite data are reliable for use in ungauged basins. Considering the simulation results, the HEC-HMS model could not fully capture the runoff patterns during the time series, and mostly underestimated the river runoff values. All three applied ML models (ELM, SVR, and LSTM) performed better than the HEC-HMS model. A correlation coefficient (R) of 0.82 was achieved for the HEC-HMS model in the validation, while R values of at least 0.9 were observed for all the ML models. The LSTM model outperformed the SVR and ELM models among the ML models in both the calibration and validation (R = 0.93) periods. Three ML models were applied to forecast 1-day-ahead rainfall-runoff prediction. The LSTM model showed the best performance among all applied ML models during the validation period, where a correlation coefficient (R) of 0.75, 0.73, and 0.71 was achieved for LSTM, SVR, and ELM, respectively. Overall, all models' accuracy increased in terms of using the coupled model. Coupled models (HEC-HMS-ELM, HEC-HMS-SVR, and HEC-HMS-LSTM) for predicting 1-day-ahead runoff discharge showed better performance than other ML models (ELM, SVR, and LSTM), where the performance of coupled models was higher than that of regular ML models during streamflow predictions. The correlation coefficient (R) of 0.91, 0.9, and 0.84 was achieved during the validation period for HEC-HMS-LSTM, HEC-HMS-SVR, and HEC-HEC-ELM, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Calibration**} & \multicolumn{3}{c}{**Validation**} & \multicolumn{3}{c}{**Improvement in Validation**} \\ \cline{2-10}  & **RMSE** & **R** & **NSE** & **RMSE** & **R** & **NSE** & **RMSE** & **R** & **NSE** \\ \hline ELM & 13.08 & 0.92 & 0.85 & 41.2 & 0.71 & 0.4 & & & \\ HEC-HMS-ELM & 10.29 & 0.96 & 0.91 & 30.5 & 0.84 & 0.67 & \(-\)26\% & 18\% & 68\% \\ SVR & 20.56 & 0.86 & 0.64 & 39.2 & 0.73 & 0.46 & & & \\ HEC-HMS-SVR & 10.26 & 0.96 & 0.91 & 25.1 & 0.90 & 0.77 & \(-\)36\% & 23\% & 67\% \\ LSTM & 13.05 & 0.92 & 0.85 & 35.74 & 0.75 & 0.55 & & & \\ HEC-HMS-LSTM & **6.93** & **0.97** & **0.96** & **23.52** & **0.91** & **0.8** & \(-\)34\% & 21\% & 46\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical measures of one-day streamflow forecasting during the calibration and validation phases at the daily scale. The bold values represent the best statistics among all the models. Figure 10: The scatter plots show the correlation between the observed and 1-day-ahead forecasted during the validation stage: (**a**) HEC-HMS-LSTM model; (**b**) HEC-HMS-SVR model; (**c**) HEC-HMS-ELM model. The overall results of the current study conclude that ML models perform better than the HEC-HMS model. The LSTM model outperformed the SVR and ELM models in both simulation and forecasting. The HEC-HMS-LSTM model was observed to be the most accurate for forecasting river runoff in the selected basin. The HEC-HMS output model can be applied to improve the performance of ML models such as the ELM, SVR, and LSTM models. This methodology can potentially be applied to catchments with similar hydrological properties around the world. All authors contributed to the study conception and design.