A Novel Deep Learning Method for Water leakage Detection Using Acoustic Features
Journal of Basic and Applied Research International, Volume 29, Issue 2,
Conservation of water is one of the major objectives for any country around the world. Water management plays a very important role in a society, as it is one of the basic needs for the mankind. Water leak detection is an important task for ensuring the safety and efficiency of water systems. Deep learning techniques, combined with real-time sensor data, can greatly enhance the accuracy and efficiency of water leak detection. Water leak detection plays a crucial role in maintaining the safety, efficiency, and sustainability of water systems. This paper presents an investigation of the capacity of deep learning methods (DL) to localize leakage in water distribution systems (WDS). Progress in real-time monitoring of WDS and DL has inspired towards new opportunities to develop data-based methods for water leak localization. However, the handlers of WDS need recommendations for the selection of the optimal DL methods as well their practical use for leakage localization. This paper contributes to this issue through an investigation of the capacity of DL methods to localize leakage in WDS. Water leak detection is crucial in conversation of water, cost saving, infrastructure protection prevention of water damage and operation efficiency. This paper shows a proposal for a system based on a wireless sensor network designed to monitor water distribution systems, such as hotel industry, irrigation systems, which, with the help of an autonomous learning algorithm, allows for precise location of water leaks. Autoencoder neural network (AE), an unsupervised DL model, is further developed to detect leak with unbalanced data. The results show AE-DL model achieved high accuracy when leaks occur in pipes inside the sensor monitoring area, while the accuracy is compromised otherwise. This observation will provide guidelines to deploy monitoring sensors to cover the desired monitoring area. A novel strategy is proposed based on multiple independent detection attempts to further increase the reliability of leak detection by the AE-DL and is found to significantly reduce the probability of false alarm.
- Water leakage detection
- deep learning
- machine learning
- water conservation
How to Cite
Amran TST, Ismail MP et al. Detection of underground water distribution piping system and leakages using ground penetrating radar (GPR). AIP Conference Proceedings, AIP Publishing LLC; 2017.
Bimpas M, Amditis A, Uzunoglu N. Detection of water leaks in supply pipes using continuous wave sensor operating at 2.45 GHz. J Appl Geophys. 2010;70(3): 226–236.
Funk A, De Oreo WB. Embedded energy in water studies study 3: end-use water demand profiles. Prepared by Aquacraft, Inc. for the California Public Utilities Commission Energy Division, Managed by California Institute for Energy and Environment, CALMAC Study ID CPU0052; 2011.
Bheki Sithole, Suvendi Rimer. Smart water leakage detection and metering device. IST-Africa 2016 Conference Proceedings Paul Cunningham and Miriam Cunningham (Eds), IIMC International Information Management Corporation; 2016.
Motaz Daadoo, Yousef-Awwad Daraghmi. Smart water leakage detection using wireless sensor networks (SWLD). International Journal of Networks and Communications; 2017.
Alberto Martini, Marco Troncossi, Alessandro Rivola. Vibration monitoring as a tool for leak detection in water distribution networks” Department of Engineering for Industry – University of Bologna Viale del Risorgimento 2, 40136 Bologna, Italy.
Awwad A, Yahya M, Albasha L, Mortula MM, Ali T. Remote Thermal Water Leakage Sensor with a Laser Communication System. IEEE Access 2020,8L163784–163796. [Cross Ref]
Ayala–Cabrera D, Herrera M, Izquierdo J, Ocaña–Levario S, Pérez–García R. GPR-Based Water Leak Models in Water Distribution Systems. Sensors. 2013;13: 15912–15936.
Noran P, Obenauf P. Asset management of a failing 36. Ductile Iron Sewage Force Main. In Pipelines 2010; American Society of Civil Engineers: Reston, WV, USA. 2010;566–576.
Mounce S, Boxall J et al. Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows. J Water Resour Plan Manag. 2010;136(3):309–318.
Mounce SR, Day AJ, Wood AS, Khan A, Widdop PD, Machell J. A neural network approach to burst detection. Water Sci Technol. 2002;45(4–5):237–246.
Mounce SR, Mounce RB et al. Novelty detection for time series data analysis in water distribution systems using support vector machines. J Hydroinf. 2010;13(4): 672–686
Dave VS, Dutta K. Neural network-based models for software effort estimation: A review. Artif. Intell. Rev. 2014;42:295–307.
Hartigan JA, Wong MA. Algorithm AS 136: A K-Means Clustering Algorithm. J. R. Stat. Soc. Ser. C. 1979;28:100–108.
LaValley, Michael P. Logistic regression. Circulation 117.18. 2008;2395-2399.
Zhang, Wei et al. Multi-modal facial affective analysis based on masked autoencoder. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2023.
Zhang, Xiufeng, Yansong Liu, and Shengjin Guo. EG-unet: Edge-guided cascaded networks for automated frontal brain segmentation in mr images. Computers in Biology and Medicine. 2023; 106891.
Lee, Geon Woo, and Hong Kook Kim. Multi-task learning u-net for single-channel speech enhancement and mask- based voice activity detection. Applied Sciences 10.9. 2020;3230.
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