Detection of underground water distribution piping system and leakages using ground penetrating radar (GPR)

Amran, Tengku Sarah Tengku; Ismail, Mohamad Pauzi; Ahmad, Megat Harun Al Rashid Megat; Amin, Mohamad Syafiq Mohd; Sani, Sham; Masenwat, Noor Azreen; Ismail, Mohamed A.; Hamid, Shu-Hazri Abdul

doi:10.1063/1.4972914

Cited by 14 publications

(6 citation statements)

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“…The total water head, which includes kinetic energy, hydraulic potential energy, and gravitational potential energy, is balanced by the energy. This equation is referenced as [27]. However, when simulating systems with fluctuating water demand, data noise, and pipeline leaks, it is common to have reduced pressures.…”

Section: Generating Data For Model Trainingmentioning

confidence: 99%

Simultaneous Pipe Leak Detection and Localization Using Attention-Based Deep Learning Autoencoder

Karimanzira

2023

Electronics

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Water distribution networks are often susceptible to pipeline leaks caused by mechanical damages, natural hazards, corrosion, and other factors. This paper focuses on the detection of leaks in water distribution networks (WDN) using a data-driven approach based on machine learning. A hybrid autoencoder neural network (AE) is developed, which utilizes unsupervised learning to address the issue of unbalanced data (as anomalies are rare events). The AE consists of a 3DCNN encoder, a ConvLSTM decoder, and a ConvLSTM future predictor, making the anomaly detection robust. Additionally, spatial and temporal attention mechanisms are employed to enhance leak localization. The AE first learns the expected behavior and subsequently detects leaks by identifying deviations from this expected behavior. To evaluate the performance of the proposed method, the Water Network Tool for Resilience (WNTR) simulator is utilized to generate water pressure and flow rate data in a water supply network. Various conditions, such as fluctuating water demands, data noise, and the presence of leaks, are considered using the pressure-driven demand (PDD) method. Datasets with and without pipe leaks are obtained, where the AE is trained using the dataset without leaks and tested using the dataset with simulated pipe leaks. The results, based on a benchmark WDN and a confusion matrix analysis, demonstrate that the proposed method successfully identifies leaks in 96% of cases and a false positive rate of 4% compared to two baselines: a multichannel CNN encoder with LSTM decoder (MC-CNN-LSTM) and a random forest and model based on supervised learning with a false positive rate of 8% and 15%, respectively. Furthermore, a real case study demonstrates the applicability of the developed model for leak detection in the operational conditions of water supply networks using inline sensor data.

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Section: Generating Data For Model Trainingmentioning

confidence: 99%

Simultaneous Pipe Leak Detection and Localization Using Attention-Based Deep Learning Autoencoder

Karimanzira

2023

Electronics

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“…For example, leak detection with acoustic signals can often be influenced by the type of leak, opening size, pipe materials and soil conditions [8]. Technology such as ground penetrating radar can detect leak around pipe but requires heavy human involvement in signal analyses [3,5,14]. The pressure or acoustic transient signals are used for pipe burst detection, since such transient signals travel along the pipe at the speed of sound starting from the burst location [41].…”

Section: Introductionmentioning

confidence: 99%

Machine learning model and strategy for fast and accurate detection of leaks in water supply network

Fan

Zhang

2021

J Infrastruct Preserv Resil

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The water supply network (WSN) is subjected to leaks that compromise its service to the communities, which, however, is challenging to identify with conventional approaches before the consequences surface. This study developed Machine Learning (ML) models to detect leaks in the WDN. Water pressure data under leaking versus non-leaking conditions were generated with holistic WSN simulation code EPANET considering factors such as the fluctuating user demands, data noise, and the extent of leaks, etc. The results indicate that Artificial Neural Network (ANN), a supervised ML model, can accurately classify leaking versus non-leaking conditions; it, however, requires balanced dataset under both leaking and non-leaking conditions, which is difficult for a real WSN that mostly operate under normal service condition. Autoencoder neural network (AE), an unsupervised ML model, is further developed to detect leak with unbalanced data. The results show AE ML 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 and is found to significantly reduce the probability of false alarm. The trained AE model and leak detection strategy is further tested on a testbed WSN and achieved promising results. The ML model and leak detection strategy can be readily deployed for in-service WSNs using data obtained with internet-of-things (IoTs) technologies such as smart meters.

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“…[5][6][7] This method can be labor-intensive, time-consuming, and cost-prohibitive. [8][9][10] Moreover, the reliability of detection are influenced by various factors including the type of leakage, size of the leakage, pipe materials, environmental conditions, and the skill level of the inspector. 11 The transient based technology uses transient pressure or acoustic signals associated with burst events.…”

Section: Introductionmentioning

confidence: 99%

An innovative machine learning based framework for water distribution network leakage detection and localization

Fan

2021

Structural Health Monitoring

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Leakages in the underground water distribution networks (WDNs) waste over 1 billion gallon of water annually in the US and cause significant socio-economic loss to our communities. However, detecting and localization leakage in a WDN remains a challenging technical problem despite of significant progresses in this domain. The progresses in machine learning (ML) provides new ways to identify the leakage by data-driven methods. However, in-service WDNs are short of labeled data under leaking conditions, which makes it infeasible to use common ML models. This study proposed a novel machine learning (ML)-based framework for WDN leak detection and localization. This new framework, named clustering-then-localization semi-supervised learning (CtL-SSL), uses the topological relationship of WDN and its leakage characteristics for WDN partition and sensors placement, and subsequently utilizes the monitoring data for leakage detection and leakage localization. The CtL-SSL framework is applied to two testbed WDNs and achieves 95% leakage detection accuracy and around 83% final leakage localization accuracy by use of unbalanced data with less than 10% leaking data. The developed CtL-SSL framework advances the leak detection strategy by alleviating the data requirements, guiding optimal sensor placement, and locating leakage via WDN leakage zone partition. It features excellent scalability, extensibility, and upgradeability for applications to various types of WDNs. It will provide valuable a tool in sustainable management of the WDNs.

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Detection of underground water distribution piping system and leakages using ground penetrating radar (GPR)

Cited by 14 publications

References 1 publication

Simultaneous Pipe Leak Detection and Localization Using Attention-Based Deep Learning Autoencoder

Simultaneous Pipe Leak Detection and Localization Using Attention-Based Deep Learning Autoencoder

Machine learning model and strategy for fast and accurate detection of leaks in water supply network

An innovative machine learning based framework for water distribution network leakage detection and localization

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