## 6 Conclusions  
Because experts are required to visit the site to detect leaks through the existing hearing leak exploration technique, there have been considerable research and development for remote leak detection, such as using IoT-based water pipeline monitoring systems. In this study, we proposed application of a CNN model to detect leaks in a water pipeline by automating leak exploration techniques that rely on the personal capabilities of experts. Using the leak sensor data, the leak detector learns the leak data, which are divided into five classes, and finally determines whether a leak has occurred. We selected a CNN model; 62,563 data samples were collected as a training dataset, and the performance was evaluated with 15,641 samples. For a reliable investigation, 50 models were developed and tested. The results presented in the paper are based on the average performance of the 50 CNN models. Five performance metrics were used for evaluation, including precision, recall, accuracy, F1 score, and MCC. For comparison, an SVM model was also applied under the same environment. The performance of the CNN model was superior to that of the SVM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Metric** & **CNN** & **SVM** \\ \hline Precision & 0.9471 & 0.8295 \\ Recall & 0.9497 & 0.7984 \\ Accuracy & 0.9580 & 0.8478 \\ F1 score & 0.9482 & 0.8099 \\ MCC & 0.9447 & 0.7986 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance summary of the CNN and SVM models, obtained by averaging over 50 runs.  
The CNN-based water pipe leak detection model proposed in this work can help develop an intelligent leak detection system. This study demonstrates the validity of artificial intelligence-based automatic leak detection. Note in this study that the data are supposed to be collected in a data hub and that the proposed model is implemented in a higher-level language. Thus, the sound samples should be periodically transmitted through wireless networks from sensor nodes, especially long-term evolution networks in this study. This results in high transmission traffic, which is not desirable.  
To overcome this disadvantage, the data traffic amount should be lowered. One way is employing a detection model that can be installed in the sensor module. The module only sends an alarm to the management center when the detection model detects an abnormal condition. For this development, the complexity of the model should be so low that it can be implemented in low-level hardware without loss of performance. This development is challenging, and the application of other machine learning models will be investigated for this development. More essential features that can reflect leakage more effectively should be studied in the time and frequency domain.  
[PERSON] and [PERSON] contributed to writing, reviewing, and editing the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1F1A1061403).  
Not applicable.  
Restrictions apply to the availability of these data. Data were obtained from AI Hub, and are available from the authors with the permission of AI Hub.  
The authors would like to express thanks to AI Hub for providing the experiment datasets.  
The authors declare no conflict of interest.