## 6 Results and Analysis
### Flood Risk Assessment  
The virtual flood forecasting, defined as the forecasting of flood occurrence with the data-driven model developed in this study, was performed using the model's results and the metrices for classification evaluation were used to evaluate the model. The criteria for flood forecasting was assumed to be above the specified elevation in Hangang River (EL. 3.9 m) and reviewed for one year, 2018. The metrices for the classification evaluation were accuracy, precision, and recall. Moreover, the confusion matrix for the binary classification and the corresponding array representation were used as shown in Table 12.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**Hyperbolic Tangent**} & \multicolumn{4}{c}{**Rectified Linear Unit**} & \multicolumn{2}{c}{**Hybrid**} \\  & **RMSE (m)** & **NSE (\%)** & **PE (m)** & **RMSE (m)** & **NSE (\%)** & **PE (m)** & **RMSE (m)** & **NSE (\%)** & **PE (m)** \\ \hline II-(A) & 0.43 & 95.2 & 0.25 & 0.42 & 95.6 & 0.23 & 0.33 & 98.8 & 0.20 \\ II-(B) & 0.45 & 92.3 & 0.25 & 0.48 & 93.4 & 0.21 & 0.31 & 98.9 & 0.19 \\ II-(C) & 0.90 & 86.8 & 0.49 & 0.88 & 88.6 & 0.41 & 0.77 & 93.6 & 0.00 \\ II-(D) & 1.23 & 80.8 & 1.05 & 1.28 & 83.3 & 0.95 & 1.13 & 86.1 & 0.89 \\ II-(E) & 1.55 & 78.2 & 1.80 & 1.48 & 79.5 & 1.90 & 1.31 & 81.2 & 1.69 \\ II-(F) & 1.86 & 66.2 & 3.11 & 1.82 & 68.3 & 3.02 & 1.52 & 74.8 & 2.97 \\ II-(G) & 2.08 & 48.3 & 3.55 & 2.03 & 52.1 & 3.23 & 1.93 & 59.3 & 2.98 \\ II-(H) & 2.33 & 38.6 & 4.12 & 2.28 & 40.9 & 4.03 & 2.05 & 53.9 & 3.84 \\ II-(I) & 2.58 & 20.7 & 4.65 & 2.66 & 25.8 & 4.55 & 2.33 & 40.8 & 4.25 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of the accuracy in various activation function in Scenario II.  
Figure 8: Partial autocorrelation of water surface elevation at Hangang River.  
The results of the classification evaluation are summarized in the Table 13 according to the activation function. In terms of the accuracy, all the activation functions have the same performance in flood forecasting. Moreover, in terms of the precision, the use of the hybrid activation function showed better performance than the existing activation functions and in terms of the recall, it was also shown that the hybrid function has a good performance for forecasting flood. However, the improvement of model performance with the hybrid activation function was not noticeable compared with the forecasting surface elevation with a hybrid activation function because the total samples of flood events were too small to evaluate the performance of model, especially the number of false non-flood conditions (\(fn\)) and false flood conditions (\(ff\)). Nevertheless, the model with the hybrid activation function was suitable for forecasting flood from the results of virtual flood forecasting because the accurate forecasting performed at a high water surface elevation with the hybrid activation function. In addition, it was considered that the accurate forecasting water surface elevation in the high water surface elevation conditions was an important factor in performing accurate flood forecasting.