## 6 Conclusions  
In conclusion, this study designed a hybrid CNN-LSTM model for pipeline leak detection that significantly enhances detection capabilities using AE signals. By integrating spatial feature extraction through a CNN with temporal feature analysis and LSTM networks, the proposed approach effectively captures both the spatial and temporal dynamics of pipeline leakages. The preprocessing step using the Savitzky-Golay filter ensures that the input signals are clean and reliable, further increasing the model's accuracy. A comparative evaluation of our proposed model against alternative techniques demonstrated its superior performance. It is clearly shown that the validation loss of the proposed method approaches zero in a smaller number of epochs as compared to the comparison methods. The validation accuracy also clearly depicts the better performance as it achieves higher accuracy in a smaller number of epochs as compared to the TD-LSTM and BiGRU models. In addition, it can be observed that the proposed model performs in a similar way regardless of the medium transported, the pressure in the pipeline, and the leak size. Furthermore, confusion matrices also highlight the better performance of the proposed method where there is no misclassification from the proposed model and there are a few misclassifications by the comparison methods. Overall, the proposed model achieved an accuracy of 99.86%, whereas the TD-LSTM and BiGRU models achieved 92.37% and 98.75% accuracies. The proposed model achieved precision of 99.85%, recall of 99.86%, and an F1 score of 99.85%. One of the limitations of this study is that the computational cost of the proposed model is slightly higher as compared to the TD-LSTM and BIGRU models, primarily due to the added complexity of combining CNN for spatial feature extraction with LSTM networks for temporal sequence analysis. This increase in complexity results in a higher computational burden, but this comes with the trade-off of achieving better accuracy in terms of overcoming the economic, environmental, and safety risks associated with pipeline leaks in smart cities and industries. Also, AE sensors cannot help to find leaks in foundations, walls, etc. Future work will explore further refinements to the model and its application to a wider range of pipeline conditions and environments. It could also focus on reducing the model's computational complexity through techniques such as model pruning, parameter sharing, or exploring lighter architectures without sacrificing the accuracy that makes the model particularly robust for leak detection tasks. Conceptualization, S.U., N.U., M.F.S., Z.A. and J.-M.K. ; methodology, S.U., N.U., M.F.S., Z.A. and J.-M.K. ; validation, S.U., N.U., M.F.S., Z.A. and J.-M.K. ; formal analysis, S.U., N.U., M.F.S., Z.A. and J.-M.K. ; resources, J.-M.K. ; writing--original draft preparation, S.U., N.U., M.F.S. and Z.A. ; writing--review and editing, J.-M.K. ; visualization, S.U., N.U., M.F.S., Z.A. and J.-M.K. ; supervision, J.-M.K. ; project administration, J.-M.K. ; funding acquisition, J.-M.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean government (MOTIE) (RS-2024-00449107, 'Development of Flexible Pipe and Connector for Hydrogen gas'). This work was also supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean government (MOTIE) (RS-2023-00232515', Development of life prediction safety technology and hydrogen embrittlement estimation of LNG pipe mixed hydrogen'). Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. Author [PERSON] was employed by the company Prognosis and Diagnostic Technologies Co., Ltd.