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Development of Leakage Detection Model and Its Application for Water Distribution Networks Using RNN-LSTM

Author

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  • Chan-Wook Lee

    (Department of Civil Engineering, The University of Suwon, Hwaseong-si 445-743, Korea)

  • Do-Guen Yoo

    (Department of Civil Engineering, The University of Suwon, Hwaseong-si 445-743, Korea)

Abstract

With the advent of the 4th Industrial Revolution, advanced measurement infrastructure and utilization technologies are being noticeably introduced into the water supply system to store and utilize measurement data. From this perspective, the leak detection technology in water supply networks is becoming increasingly vital to sustainable water resource management and the clean water supply worldwide. In particular, leakage detection of buried pipelines is rated as a very challenging research topic given the current level of technology. However, leakage in buried underground pipelines is rated as a very challenging research topic given the current level of technology. Therefore, a data-driven leak detection model was developed through this study using deep learning technology based on inflow meter data. Multiple threshold-based models were applied to reduce the RNN-LSTM (Recurrent Neural Networks–Long Short-Term Memory models) deep learning and false prediction range, which is programmed in conjunction with the Python language and Google Colaboratory (a big data analysis tool). The developed model consists of flow pattern shape extraction, RNN-LSTM-based flow prediction, and threshold setting modules. The developed model was applied to the actual leakage accident data, followed by the performance evaluation. As a result, the leak was recognized at most points immediately after the accident. The performance of leak detection was evaluated by a Confusion matrix and showed more than 90% accuracy at all points except singularities. Therefore, the developed model can be used as a critical software technology to proactively identify various at present with smart water infrastructure being introduced. In addition, this model is highly scalable as it can consider various operational situations based on the expert system, and it can also efficiently reflect the results of pipe network analysis across different scenarios.

Suggested Citation

  • Chan-Wook Lee & Do-Guen Yoo, 2021. "Development of Leakage Detection Model and Its Application for Water Distribution Networks Using RNN-LSTM," Sustainability, MDPI, vol. 13(16), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9262-:d:616674
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    References listed on IDEAS

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    1. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    2. Erfan Hajibandeh & Sara Nazif, 2018. "Pressure Zoning Approach for Leak Detection in Water Distribution Systems Based on a Multi Objective Ant Colony Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2287-2300, May.
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    Cited by:

    1. Konstantinos Glynis & Zoran Kapelan & Martijn Bakker & Riccardo Taormina, 2023. "Leveraging Transfer Learning in LSTM Neural Networks for Data-Efficient Burst Detection in Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(15), pages 5953-5972, December.
    2. Hyeong-Suk Kim & Dooyong Choi & Do-Guen Yoo & Kyoung-Pil Kim, 2022. "Hyperparameter Sensitivity Analysis of Deep Learning-Based Pipe Burst Detection Model for Multiregional Water Supply Networks," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    3. Hyeong-Suk Kim & Dooyong Choi & Do-Guen Yoo & Kyoung-Pil Kim, 2022. "Development of the Methodology for Pipe Burst Detection in Multi-Regional Water Supply Networks Using Sensor Network Maps and Deep Neural Networks," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    4. Sanghoon Jun & Kevin E. Lansey, 2023. "Convolutional Neural Network for Burst Detection in Smart Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3729-3743, July.
    5. Chia-Cheng Shiu & Chih-Chung Chung & Tzuping Chiang, 2024. "Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 323-341, January.
    6. Ryul Kim & Young Hwan Choi, 2023. "The Development of a Data-Based Leakage Pinpoint Detection Technique for Water Distribution Systems," Mathematics, MDPI, vol. 11(9), pages 1-18, May.
    7. Joohee Park & Seulgi Kang & Seongjoon Byeon, 2025. "Analysis of Regional Characteristics of Climate Change Factors Affecting Water Distribution Pipe Leakage," Sustainability, MDPI, vol. 17(2), pages 1-22, January.

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