## 3 Results and Discussion  
### Selection of Oceanic-Atmospheric Indices  
Table 3 shows the best-fitting predictors having the highest correlations with \(p\)-values of less than 5% and VIF of less than 5. We described the correlation strength between predictors and reservoir inflow of 0.80 and higher as \"very strong\", those from 0.60 to 0.79 as \"strong\" and those from 0.40  
Figure 4: Flowchart of small basin scale dam inflow prediction by multiple linear regression (MLR), a support vector machine (SVM) and ANN. to 0.59 as moderate correlations [51]. In January, two predictors were selected, Tropical Northern Atlantic index (TNA; lag-10 months) and extended reconstructed sea surface temperature (ERSST; lag-3 months), and showed strong correlation (\(-\)0.67, 0.61) with \(p\)-values of less than 1%. Additionally, WP (lag-4 months) and the multivariate ENSO index (MEI; lag-8 months) have strong correlations (\(-\)0.67 and 0.68) with \(p\)-values of less than 1% in February. The East Atlantic pattern (EA) was well correlated during the month of the March (\(-\)0.71) and May (0.67). Four climate indices were selected in April with NINA3 (lag-10 months) showing a very strong correlation (0.83), and the remaining predictor's correlation coefficients lie between the values 0.44 and 0.66. The ERSST (lag-3 months) has a strong correlation in November (0.75) and December (0.66). A different set of climate factors was selected for each month as is found in Table 3. The lag month as little as 3 months to as long as 12 months were found for climate factors exhibiting the different levels of lags of climate indices that has influence on streamflow. The spring season (March-May) were found to be the months that take the most advantage of good number of climate indices (four) as predictors for streamflow. A similar pattern of results was presented in previous work [52], indicating the variability of climatic factors that have influences on each month for medium-range forecasting on the study area. Among the climate indices considered, TNA, Arctic oscillation (AO), EA and ERSST were found to be selected repeatedly more than once with different lagged months as major predictors in several months. The ERSST, which is the climate index representing the sea surface temperature, was identified to be most frequent predictor (selected in five different months) that has the influence on streamflow. In many papers, ocean SST were identified to influence streamflow [20; 53; 54]. The correlation analysis between the ERSST value of the waters around the Korean Peninsula and the reservoir inflow was investigated in order to find the region highly correlated with the inflow with 3-month lead time. As a result, ERSST was identified to be useful for lead time forecasting of reservoir inflow except February and April. Table 4 lists the selected locations for ERSST for the months being considered in this study. Figure 5 shows the moving correlation coefficients with lags considered between Q\({}^{\prime}\) and Qs for the entire period 1998-2016. Except the ERSST(Mar, Dec) and TNA(Nov), the remaining factors exceeded a significance level of 95%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Month** & **Jan** & **Feb** & **Mar** & **Apr** & **May** & **Nov** & **Dec** \\ \hline Locations & 48\({}^{\circ}\) N, & 40\({}^{\circ}\) N, & 26\({}^{\circ}\) N, & 32\({}^{\circ}\) N, & 46\({}^{\circ}\) N, & 60\({}^{\circ}\) N, & 48\({}^{\circ}\) N, \\ selected & 210\({}^{\circ}\) E & 150\({}^{\circ}\) E & 244\({}^{\circ}\) E & 198\({}^{\circ}\) E & 146\({}^{\circ}\) E & 152\({}^{\circ}\) E & 210\({}^{\circ}\) E \\ Correlation & 0.61 & 0.40 & 0.45 & 0.64 & 0.47 & 0.75 & 0.66 \\ coefficient & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Selected locations from the global climate data for the extended reconstructed sea surface temperature (ERSST).