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 strategy’s 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.

KEYWORDS:

• Leakage
• artificial intelligence
• integrated water management

Disclosure statement

No potential conflict of interest was reported by the authors.