## 3 Application and Results
### Evaluation of Water Loss Management Efficiency and Determination of Formulation of Water Loss  
In this study, the increase in the flow rate and the decrease in the leakage rate according to the data classification criteria (higher than 10% of leakage ratio, less than 80% of the revenue water ratio) were analyzed according to four input indices (PR, MR, LR, LM). Through this, a leakage management model formulation was derived according to the increase in the revenue water ratio and the decrease in the leakage ratio for input indicators. In order to derive a reliable leakage management model formulation, MRA and DNN were used, and Table 5 shows the result of deriving the leakage management model formulation according to the data classification criteria. As a result of the leakage management model formulation, the coefficient of determination (R) of the eight classification criteria was distributed from 0.32 to 0.82, and an average value of 0.55 for MRA and 0.61 for DNN. Among the eight groups, the highest coefficient of determination in both analysis through MRA and DNN is Group 1, with a leakage ratio of less than 10%. In the case of Group 1, it depends on PR, MR, LD, etc., to reduce the leakage ratio, and it is confirmed that PR has the greatest influence on reducing the leakage ratio. In addition, if the leakage ratio is less than 10%, the revenue water ratio is high. In addition, it was confirmed that PR among the four leakage reduction activities was essential for reducing the leakage ratio and increasing the revenue water ratio in  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study Area**} & \multirow{2}{*}{ \begin{tabular}{c} **Leakage Ratio** \\ **Reduction (\%)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Pipe** \\ **Replacement** \\ **(PR)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Meter** \\ **Replacement** \\ **(MR)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Leakage** \\ **Repair** \\ **(LR)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **T-Score** \\ **(LD)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **T-Score** \\ **for PR** \\ \end{tabular}  } & \multirow{2}{*}{
\begin{tabular}{c} **T-Score** \\ **for LR  
all classification criteria, except for Group 6, where the revenue water ratio was 60-70%. According to Group 8 in contrast to Group 1, LD was shown to be the most significant factor to increase the revenue water ratio. In the case of Group 8, the revenue water ratio is already high, and because PR, MR, and LR are judged to be in progress, the revenue water ratio is expected to decrease if no leakage reduction activities are carried out. This is because the model formula was derived as a negative number (\(-0.47\)), as in Group 1. The average value for the coefficient of determination on each data classification criteria of MRA and DNN were 0.55 and 0.61. In all groups, except for Groups 1 and 2, the DNN model of the coefficient of determination was high. Through the above results, it can be judged that DNN is a suitable model for the formulation of water loss management. However, the conditions required for performing each model are different, and the results can also be derived differently depending on data processing, such as model parameters and training data mining. As a result of comparison of the models applied in this study, it was found that the application of an artificial intelligence algorithm similar to DNN rather than MRA is more appropriate when there is a large variance, such as water loss management data, among various artificial intelligence algorithms. Therefore, in order to derive better results, the analysis result can be improved through sensitivity analysis of the algorithm parameters and various training data classifications.