Pipe network leak detection: comparison between statistical and machine learning techniques

Abstract

This paper investigates an inverse analysis technique to find leaks in water networks and compares different solution strategies. Although a number of strategies have been proposed by different authors to identify leaks on a vast selection of pipe networks, limited research has been done to compare strategies and point out their weakness. Three strategies, a Bayesian Probabilistic Analysis, a Support Vector machine and, an Artificial Neural Network were combined with the inverse analysis technique on different numerical and experimental networks to point out each strategies weakness. Two numerical networks are investigated and one experimental network. It is shown that the Bayesian Probabilistic Analysis struggles to find unique solutions when a few observations are available, while the Support Vector Machine and the Artificial Neural Network struggle when only flow measurements are available. Additionally it is shown that the Artificial Neural Network struggles to estimate unique solutions for leak size and location.

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

“…This research aimed at investigating the capacity of machine learning methods to localize the position of leakages in water distribution systems using flow and water pressure data. Following the methodology proposed by different scholars [15,16,19,23], the hydraulic software EPANET was used to create data related to different scenarios of leakage in the water distribution system of the scientific campus. For each leakage scenario, EPANT provided the water flow from the supply sections and the pressure in five hydraulic areas of the campus.…”

“…Van der Walt et al [23] analyzed the capacity of Bayesian probabilistic analysis, the support vector machine, and an artificial neural network to detect and localize water leakage from pressure and flow data. These methods were compared to data generated from numerical modeling and laboratory tests.…”

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

“…Beberapa penelitian lainnya yang telah dikembangkan, Y. Liu, dkk, menggunakan metode SVM dan Fitur Frekuensi Waktu untuk melakukan analisa sinyal yang dihasilkan oleh beberapa node sensor [7]. Penelitian lain juga melakukan perbandingan dengan beberapa metode menggunakan deviasi hasil pengukuran tekanan dan laju aliran sebagai data yang akan diolah kedalam metode algoritma Bayesian Probabilistic Analysis, SVM, dan Artificial Neural Network (ANN) [8].…”

Leak diagnosis in pipelines using a combined artificial neural network approach

“…For both data sets, the input data for the neural network was obtained from flow and pressure sensors installed at the ends of the pipeline as these measures are the most used for leak detection and location (Wong et al, 2018;Van der Walt et al, 2018;Raei et al, 2019). Furthermore, to guarantee the data quality of the experimental set-up, the location of the sensors was done carefully according to the hydraulic handbook (Livelli, 2010).…”