Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems

Mashhadi, Neda; Shahrour, Isam; Attoue, Nivine; Khattabi, Jamal El; Aljer, Ammar

doi:10.3390/smartcities4040069

Cited by 43 publications

(14 citation statements)

References 35 publications

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“…In contrast, the EPANET hydraulic model of the University of Port Harcourt Choba campus is shown in Figure 1. The water distribution network was split into two zones: Zone 1 (LZ1), which contains eight candidate nodes, and Zone 2 (LZ2), which contains seven candidate nodes [15]. The water distribution network was split into two zones: Zone 1 (LZ1), which contains eight candidate nodes, and Zone 2 (LZ2), which contains seven candidate nodes [15].…”

Section: Data Generationmentioning

confidence: 99%

“…The water distribution network was split into two zones: Zone 1 (LZ1), which contains eight candidate nodes, and Zone 2 (LZ2), which contains seven candidate nodes [15]. The water distribution network was split into two zones: Zone 1 (LZ1), which contains eight candidate nodes, and Zone 2 (LZ2), which contains seven candidate nodes [15]. The emitter coefficient used in the simulation of leak occurrence in the nodes is based on the classical Torricelli equation for flow through an orifice.…”

Section: Data Generationmentioning

confidence: 99%

“…The use of time series and pattern recognition algorithms to predict leaks and for the localization of zones where leaks are detected has been explored in the literature [15]. Lee and Yoo [16] created a data-driven leak detection model using the deep learning technique; the model was used to simulate a real leaking accident, and its performance was assessed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of a Fault Detection and Localization Model for a Water Distribution Network

Onukwube,

Aikhuele,

Sorooshian

2024

Applied Sciences

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Water distribution networks are complex systems that aid in the delivery of water to residential and non-residential areas. However, the networks can be affected by different types of faults, which could lead to the wastage of treated water. As such, there is a need to develop a reliable leakage detection and localization system that can detect leak occurrences in the network. This study, using a simulated dataset from EPANET, presents the application of supervised machine learning classifiers for leak detection and localization in the water distribution network of the University of Port Harcourt Choba campus. The study compared three machine learning classification tools that are used in pattern recognition analysis: the support vector machine, k-nearest neighbor, and artificial neural network. The robustness and effectiveness of the proposed approach are compared with those of the performance of the classifiers for leakage detection in the network of the case study. The results show that the support vector machine performs the best, with 79% accuracy, while the respective accuracies for the remaining classifiers are 70% for the k-nearest neighbor and 61% for the artificial neural networks. The high accuracy demonstrated by the models shows that they are able to detect and address issues relating to fault detection in a water distribution network. This model could provide a leakage detection system to be applied to buildings for the efficient management of water in their networks.

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Section: Data Generationmentioning

confidence: 99%

Section: Data Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of a Fault Detection and Localization Model for a Water Distribution Network

Onukwube,

Aikhuele,

Sorooshian

2024

Applied Sciences

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“…Dalam beberapa penelitian yang telah dilakukan untuk mendeteksi kebocoran pipa menggunakan algoritma Support Vector Machine (SVM) [1][2]. Ada juga beberapa peneliti yang menggunakan Neural Network dalam mendeteksi kebocoran pipa [3]. Penelitian selanjutnya menggunakan data time series tekanan dan volume air dan metode regresi sebagai algoritmanya [4], ada pula peneliti dalam deteksi kebocoran pipa dan lokasinya menggunakan metode berbasis fuzzy [5], maupun penggunaan metode K-Nearest Neighboor [6].…”

Section: Pendahuluanunclassified

Deteksi Kebocoran Pipa Air Menggunakan Machine Learning dengan Jaringan Nirkabel IEEE 802.15.4

Saputra¹,

Rasyid

Hadi³

2022

ISI

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Pipa adalah cara paling ekonomis dan paling aman dalam mendistribusikan hasil produk seperti air, petrokimia, gas, dan cairan lainnya. Terlepas dari manfaat tersebut, ternyata pipa memiliki ancaman yaitu potensi kebocoran. Artikel ini membahas pendeteksian kebocoran pipa air menggunakan parameter debit aliran. Pengujian dilakukan pada dua format dataset, menggunakan raw dataset dan process dataset menggunakan metode volume balance. Pada proses pembelajaran ada beberapa hal yang perlu disoroti seperti pemilihan tipe dataset, pre-processing dengan menormalisasi dataset, dan menerapkan metode fungsi kernel untuk meningkatkan kinerja akurasi prediksi ukuran dan lokasi kebocoran pipa. Dataset dilatih menggunakan algortima SVM untuk mengklasifikasikan ukuran dan lokasi kebocoran pipa. Hasil klasfikasi ukuran kebocoran dengan fungsi kernel polynomial pada raw dataset mencapai akurasi sebesar 98,25%, recall 99,1%, presisi 99,8%, dan F-measure 99,5%. Sedangkan fungsi kernel Radial Basis Function pada process dataset mencapai akurasi tertinggi sebesar 89,7%, recall 94,4%, presisi 95,4%, dan F-measure 94,6%. Dalam hal mengidentifkasikan lokasi kebocoran, fungsi kernel polynomial pada raw dataset meningkatkan akurasi sebesar 88,96%, recall 94,7%, presisi 91,5%, dan F-measure 92,8%. Sedangkan fungsi kernel polynomial pada process dataset mencapai akurasi sebesar 74,42%, recall 74,1%, presisi 72,8%, dan F-measure 71,3%.

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“…Researchers of this study proposed a data based leak detection model for leak identification that showed a good result and has an accuracy of 90% at all points except singularities by the confusion matrix method (3) . Researchers of this study represent an investigation of the capacity of six machine learning methods presented by the data generated using EPANET software, indicate that the supervised logistic regression and random forest method performed well to localize the leakage (4) . A multi-strategy ensemble learning(MEL) was presented in this research as an effective solution for an improvement in leak detection using acoustic techniques (5) .…”

Section: Introductionmentioning

confidence: 95%

An Experimental Study to Locate Leakage in the Water Distribution Network using Real-time Wireless Sensor and Machine Learning Algorithm

Dalal¹,

Vasani²

2022

IJST

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Objective: To pinpoint the leakage location using a machine learning algorithm on a real-time basis. Method: A laboratory experimental model was developed using wireless sensors for real-time data collection and monitoring the changes in the pressure and flow in presence of leakage under a different scenario. Modification in laboratory model was made considering Loop network of distribution pipes as compared to the simple experimental model. The model has been validated in EPANET software. The machine learning algorithm, along with the K-fold approach has been used to locate leakage and it was compared with other algorithms like Support Vector Machine (SVM), logistic regression, multilayer perceptron, Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), Principal Component Analysis (PCA), Artificial Neural Network (ANN), Random Forest, Gradient Boosting Tree (GBT) Classification, and Convolutional Neural Network (CNN). Findings: In this research, a system was developed based on real-time data received by Wireless Sensor Network (WSN) was demonstrated and it is able to find even a small leak by monitoring pressure and flow. The K-fold approach in machine learning algorithm has been used to locate the leakage in different variations made in the experimental model in terms of pressure, leak size, cross-section of pipe, and profile level of the pipe network. A comparison was also presented with other machine learning algorithms of recent research in terms of accuracy, size of the opening for the leak, type of experimental model used, and variables considered to locate the leakage. Novelty: It is the first research kind of work that shows the average accuracy of 78% to locate leakage based on the real-time experimental data in a loop network using the K-Fold approach.

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Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems

Cited by 43 publications

References 35 publications

Development of a Fault Detection and Localization Model for a Water Distribution Network

Development of a Fault Detection and Localization Model for a Water Distribution Network

Deteksi Kebocoran Pipa Air Menggunakan Machine Learning dengan Jaringan Nirkabel IEEE 802.15.4

An Experimental Study to Locate Leakage in the Water Distribution Network using Real-time Wireless Sensor and Machine Learning Algorithm

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