## 3 Results and Discussion
### Individual Model Performance  
The performance of each model in terms of r (correlation coefficient) and S are listed in Table 5. The r values of the three models ranged from 0.62 to 0.90, which shows that the pattern of the observations was well simulated. The r value of 0.7 or more means a strong relationship between the observed and predicted values based on the criteria given by [PERSON] et al. (2016). In terms of skill score, the monthly prediction skill score was larger than 0 for all three models and showed some predictive skill. The S values of the MLR model ranged from 0.21 to 0.55, the ANN model ranged from 0.20 to 0.52, and the SVM from 0.21 to 0.56. Except for January and December, the maximum prediction score of the models was above 0.4. One of the reasons why the forecasting score was low in January and December compared with the other months is that the variation in monthly inflow was large, which made the model prediction more difficult for these two months. The MLR model has a relatively simpler method than the other two models, however the forecast performance shows similar results and is considered to be an applicable and useful method with short computation time. Among the three models, MLR model depicted best results in April (r = 0.86, S = 0.49), and ANN model showed the best result in February (r = 0.83, S = 0.44), May (r = 0.81, S = 0.41) and November (r = 0.88, S = 0.52). SVM model indicated the best skill score in the cases of January (r = 0.67, S = 0.26), March (r = 0.9, S = 0.56) and December (r = 0.76, S = 0.34). The comparison of the observed and predicted values is shown in Figure 6. It can be concluded that the statistical method using climatic indices to predict dam inflow for small basin scale gives reasonable forecasts, with a 3-months lead time.  
With the increase of observed data, the accuracy of forecasts will be improved. This issue of the effect of the training set size on the forecast performance of data-driven models is well confirmed in the literature. [PERSON] et al. (2016) showed for the remotely sensed data set the classification accuracy was increased significantly as a result of increasing the number of training cases using ANN method. [PERSON] (2016) discussed the effect of training set size for the performance of neural networks of classification and showed the tendency that multilayer perceptrons have better performance in relatively larger training data sets. [PERSON] et al. (2016) investigate the dependency between sample size and accuracy of SVM. As a number of cases increases, SVM showed a substantial improvement in performance. [PERSON] et al. (2017) analyzed the influence of training set size on the prediction accuracy of SVM model and indicated that the predictions became stable with increasing size of training sets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multicolumn{2}{c}{**MLR**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**SVM**} \\  & **r** & **S** & **r** & **S** & **r** & **S** \\ \hline Jan & 0.65 & 0.21 & 0.67 & 0.20 & 0.67 & **0.26** \\ Feb & 0.69 & 0.26 & 0.83 & **0.44** & 0.62 & 0.21 \\ Mar & 0.89 & 0.55 & 0.86 & 0.49 & 0.90 & **0.56** \\ Apr & 0.86 & **0.49** & 0.76 & 0.35 & 0.83 & 0.44 \\ May & 0.78 & 0.33 & 0.81 & **0.41** & 0.71 & 0.30 \\ Nov & 0.81 & 0.41 & 0.88 & **0.52** & 0.80 & 0.32 \\ Dec & 0.75 & 0.31 & 0.75 & 0.27 & 0.76 & **0.34** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Monthly forecast skill scores (r and S) between the observed and predicted dam inflow with a 3-months lead time. The bold numbers represent the best prediction skill score of each month.  
Figure 6: \(Cont\).  
Figure 6: _Cont._