## 2 Background Study on Pipeline Leak Detection Methods
### Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sl No** & **Models** & **Prediction** &
\begin{tabular}{c} **Precision** \\ **Accuracy** \\ \end{tabular} & **Recall** & **F-Score** & **R2** \\ \hline
1 & Modified MFCC-2 DCNN & 84.97 & 83.56 & 83.99 & 83.77 & 85.72 \\
2 & MFCC-proposed 2 DCNN & 90.56 & 89.98 & 89.05 & 89.51 & 90.12 \\
3 & EMD–MFCC-proposed & 98.63 & 98.66 & 98.63 & 98.64 & 98.37 \\  & 2 DCNN & & & & & \\
4 & EEMD–MFCC-proposed & 98.82 & 98.81 & 98.76 & 98.78 & 98.68 \\  & 2 DCNN & & & & & \\
5 & CEEMDAN–MFCC- & 98.86 & 98.83 & 98.82 & 98.82 & 98.72 \\  & 2 DCNN & & & & & \\
6 & **Proposed novel EMD** & **99.53** & **98.89** & **98.85** & **98.87** & **99.01** \\  & **with optimal mode** & & & & & \\
**selector, MFCC, and** & & & & & & \\
**2 DCNN** & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for leak detection and localization at 2-bar pressure. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sl No** & **Models** &
\begin{tabular}{c} **Prediction** \\ **Accuracy** \\ \end{tabular} & **Precision** & **Recall** & **F-Score** & **R2** \\ \hline
1 & MFCC–2 DCNN [10] & 96.95 & 96.23 & 95.98 & 96.1 & 96.78 \\
2 & MFCC-proposed 2 DCNN & 99.41 & 99.33 & 99.31 & 99.32 & 97.22 \\
3 & EMD–MFCC-proposed & 99.68 & 99.58 & 99.58 & 99.58 & 98.33 \\  & 2 DCNN & & & & & \\
4 & EEMD–MFCC-proposed & 99.78 & 99.71 & 99.73 & 99.72 & 98.88 \\  & 2 DCNN & & & & & \\
5 & CEEMDAN–MFCC- & 99.98 & 99.97 & 99.87 & 99.92 & 99.97 \\  & proposed 2 DCNN & & & & & \\
6 & **Proposed novel EMD** & **99.99** & **99.99** & **99.99** & **99.99** \\  & **with optimal mode** & & & & & \\
**selector, MFCC, and** & & & & & \\
**2 DCNN** & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results for leak detection at 2-bar pressure. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sl No** & **Models** &
\begin{tabular}{c} **Prediction** \\ **Accuracy** \\ \end{tabular} & **Precision** & **Recall** & **F-Score** & **R2** \\ \hline
1 & MFCC–2 DCNN [10] & 97.53 & 96.68 & 96.12 & 96.39 & 96.78 \\
2 & MFCC-proposed 2 DCNN & 99.31 & 99.21 & 99.29 & 99.25 & 97.22 \\
3 & EMD–MFCC-proposed & 99.72 & 99.62 & 99.69 & 99.65 & 98.88 \\  & 2 DCNN & & & & \\
5 & CEEMDAN–MFCC- & 99.86 & 99.82 & 99.84 & 99.83 & 99.44 \\  & 2 DCNN & & & & \\
6 & **Proposed** novel EMD** & 99.89 & 99.91 & 99.92 & 99.91 & 99.88 \\
6 & **Proposed novel EMD** & **99.99** & **99.99** & **99.99** & **99.99** \\  & **with optimal mode** & & & & \\
**selector, MFCC, and** & & & & & \\
**2 DCNN** & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results for leak detection at 3-bar pressure. Tables 2 and 3 give the leak detection results at 2-bar and 3-bar pressure, respectively. All the models give good results. The detection methods utilizing the noise removal methods give better accuracy, and the proposed novel EMD with an optimal mode selector, an MFCC, and a 2 DCNN detects the leak with the best accuracy. Tables 4 and 5 give the leak detection and localization results at 2-bar and 3-bar pressure. These tables clearly show the effect of the denoising techniques and the importance of the optimal node selector algorithm. The prediction accuracy, precision, recall, and F-score of the EMD variants are very close. The R-square metric helps to reveal how well the model fits and shows visible differences. The R-square metric value increases as the level of the EMD variant changes. The CEEMDAN gives a better R-square value than the EEMD and EMD. It shows that EEMD and CEEMDAN helped to solve the mode mixing issue. The proposed novel EMD with an optimal mode selector gives the best prediction accuracy, precision, recall, F-score, and R-square as the algorithm is designed to choose the optimal IMF based on the requirements. This paper also utilizes cross-correlation to validate the proposed novel EMD with an optimal mode selector, an MFCC, and a 2 DCNN. Four samples from the 2-bar pressure dataset assist in implementing the cross-correlation.