## 3 Results and Discussion
### One-Day-Ahead ML and HEC-ML  
Figure 7 shows the 1-day-ahead forecasts using ELM, SVR, and LSTM models in the calibration (from January 2005 to December 2006) and validation (from January 2007 to December 2007) periods, where the predicted runoff discharges are plotted along with the observed data. Table 4 shows the performance of the 1-day ahead predictions in the calibration and validation periods among the coupled and data-driven models. The LSTM model showed the best performance among all applied ML models during the validation period, where the RMSE, R, and NSE were 35.74, 0.75, and 0.55, respectively. Thus, the accuracy ranking of the ML model was LSTM > SVR > ELM. Figure 8 compares the 1-day-ahead forecasts using the HEC-HMS-ELM, HEC-HMS-SVR, and HEC-HMS-LSTM in the coupled models. As shown in Table 4, the statistical measures for the calibration and validation periods showed that the HEC-HMS-LSTM performed better than the HEC-HMS-SVR and HEC-HMS-ELM. The values of RMSE, R, and NSE for the HEC-HMS-LSTM were 23.52, 0.91, and 0.8, respectively. The results showed that additional inputs from the HEC-HMS predictions effectively reduced the accumulated errors in the ML models. The maximum improvements during the validation period are presented in Table 4.  
According to the data distribution from Figures 7 and 9, coupled models (HEC-HMS-ELM, HEC-HMS-SVR, and HEC-HMS-LSTM) were superior for predicting 1-day-ahead runoff discharge than other ML models (ELM, SVR, and LSTM), where the performance \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Calibration**} & \multicolumn{3}{c}{**Validation**} \\ \cline{2-7}  & **RMSE** & **R** & **NSE** & **RMSE** & **R** & **NSE** \\ \hline HEC-HMS & 19.78 & 0.92 & 0.78 & 25.55 & 0.82 & 0.62 \\ ELM & 18.84 & 0.93 & 0.79 & 21.94 & 0.90 & 0.72 \\ SVR & 18.85 & 0.94 & 0.80 & 19.87 & 0.91 & 0.77 \\ LSTM & **17.16** & **0.98** & **0.96** & **18.84** & **0.93** & **0.79** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical measures of streamflow simulation during the calibration and validation phases at the daily scale. The bold values represent the best statistics among all the models. Figure 7: Comparison of forecasting and observed daily streamflow for one-day prediction during the calibration and validation periods using ML models. The blue vertical dashed line divides the time series into calibration and validation periods. A zoom-in of the blue circle area investigates the model’s performance in March 2007.  
of coupled models was higher than that of regular ML models during runoff discharge predictions. The scatter plots in Figure 8 show that all three ML models overestimated the discharge peak several times. Figure 10 shows that HEC-HMS-ELM underestimated runoff discharges and HEC-HMS-SVR overestimated runoff discharges, whereas the accuracy of HEC-HMS-LSTM was more acceptable. This result shows that the HEC-HMS-LSTM model performed better than the coupled models. Table 4 clearly shows the improvements in the coupled models. The measure of R was improved from 18 to 23 percent. The highest improvement was related to the SVR model (23%), while the lowest improvement was observed for the ELM model (18%). Generally, all models were improved by the coupled models, but the same pattern was followed by all models in the calibration and validation periods. Among the coupled models, LSTM outperformed the SVR and ELM. Therefore, the accuracy ranking was HEC-HMS-LSTM > HEC-HMS-SVR > HEC-HMS-ELM for the selected basin at the daily scale river runoff. The results clearly show that using HEC-HMS output as the ML model input can improve forecasting accuracy. Figure 8: The scatter plots of one-day streamflow forecasting in the validation step: (a) LSTM model; (b) SVR model; (c) ELM model. Figure 9: Daily forecast streamflow performance at the lead-time 1 day using coupled models during the calibration and validation periods. The blue vertical dashed line divides the time series into calibration and validation periods.