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.
Highlights
Water distribution networks (WDN), one of the social infrastructure facilities, are installed and operated underground in many large spaces to distribute, transport, and supply purified water from a water source to the faucet of each customer
The developed model consists of flow pattern shape extraction, RNN-LSTM-based flow prediction, and threshold setting modules
The leak detection technology in water supply networks is increasingly vital to sustainable water resource management and clean water supplies worldwide
Summary
Water distribution networks (WDN), one of the social infrastructure facilities, are installed and operated underground in many large spaces to distribute, transport, and supply purified water from a water source to the faucet of each customer. Long-buried pipelines in water distribution networks may cause various abnormal situations in the system, such as water loss through large and small-scale continuous leaks (reduction of water flow rate), water quality problems by scale or corrosion accumulated in the pipelines, and poor water outflow due to narrowed flow area (low water pressure). The leakage rate of waterworks nationwide is 10.8% (about 720 million m3 ). It is essential to efficiently maintain and manage the water distribution network system to detect and repair leaks immediately to remodel or replace old pipelines as a preventive measure. The leak detection technology in water supply networks is increasingly vital to sustainable water resource management and clean water supplies worldwide. Leakage detection in buried pipelines is rated as a very challenging research topic given the current level of technology [1]. In the event of leaks due to an accident or sudden
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
