Leak localization in water distribution networks using pressure and data-driven classifier approach

Leaks in water distribution networks (WDNs) are one of the main reasons for water loss during fluid transportation. Considering the worldwide problem of water scarcity, added to the challenges that a growing population brings, minimizing water losses through leak detection and localization, timely and efficiently using advanced techniques is an urgent humanitarian need. There are numerous methods being used to localize water leaks in WDNs through constructing hydraulic models or analyzing flow/pressure deviations between the observed data and the estimated values. However, from the application perspective, it is very practical to implement an approach which does not rely too much on measurements and complex models with reasonable computation demand. Under this context, this paper presents a novel method for leak localization which uses a data-driven approach based on limit pressure measurements in WDNs with two stages included: (1) Two different machine learning classifiers based on linear discriminant analysis (LDA) and neural networks (NNET) are developed to determine the probabilities of each node having a leak inside a WDN; (2) Bayesian temporal reasoning is applied afterwards to rescale the probabilities of each possible leak location at each time step after a leak is detected, with the aim of improving the localization accuracy. As an initial illustration, the hypothetical benchmark Hanoi district metered area (DMA) is used as the case study to test the performance of the proposed approach. Using the fitting accuracy and average topological distance (ATD) as performance indicators, the preliminary results reaches more than 80% accuracy in the best cases.Peer ReviewedPostprint (published version

Leak localization in water distribution networks using pressure and a data-driven classifier approach

This report proposes a new data-driven method based on limit pressure measurements to deal with leak localization problem in WDNs, or in partitioned zones called District Metered Areas (DMAs).Postprint (updated version

Leak detection in drinking water network using pressure-based classifier

Water leakage in drinking water networks (DWN) is the main reason for water loss in water networks. Considering the worldwide problem of water scarcity added to the challenges that a growing population brings, minimizing the water losses through detection of water leakages in DWN using efficient techniques is an urgent humanitarian need. This paper proposes a new data-driven leak detection method of defining classifiers based on limit pressure measurements in DWNs. This can get rid of complexities and application constraints of the model-based approach. The end result is an average 60% detection accuracy with limited data requirements. The proposed approach is applied to Hanoi DWN.Peer ReviewedPostprint (author's final draft

Leak localization in water distribution networks using pressure and data-driven classifier approach

Leaks in water distribution networks (WDNs) are one of the main reasons for water loss during fluid transportation. Considering the worldwide problem of water scarcity, added to the challenges that a growing population brings, minimizing water losses through leak detection and localization, timely and efficiently using advanced techniques is an urgent humanitarian need. There are numerous methods being used to localize water leaks in WDNs through constructing hydraulic models or analyzing flow/pressure deviations between the observed data and the estimated values. However, from the application perspective, it is very practical to implement an approach which does not rely too much on measurements and complex models with reasonable computation demand. Under this context, this paper presents a novel method for leak localization which uses a data-driven approach based on limit pressure measurements in WDNs with two stages included: (1) Two different machine learning classifiers based on linear discriminant analysis (LDA) and neural networks (NNET) are developed to determine the probabilities of each node having a leak inside a WDN; (2) Bayesian temporal reasoning is applied afterwards to rescale the probabilities of each possible leak location at each time step after a leak is detected, with the aim of improving the localization accuracy. As an initial illustration, the hypothetical benchmark Hanoi district metered area (DMA) is used as the case study to test the performance of the proposed approach. Using the fitting accuracy and average topological distance (ATD) as performance indicators, the preliminary results reaches more than 80% accuracy in the best cases.Peer Reviewe

Factors influencing the stormwater quality model of sewer networks and a case study of Louis Fargue urban catchment in Bordeaux, France

Pollution caused by combined sewer overflows has become a global threat to the environment. Under this challenge, quality-based real-time control (RTC) is considered as an effective approach to minimize pollution through generating optimal operation strategies for the sewer infrastructure. To suit the fast computation requirement of RTC implementation, simplified quality models are required. However, due to the hydrological complexity, it is not easy to develop simplified quality models which are amenable to be used in real-time computations. Under this context, this paper contributes 1) a preliminary analysis of influencing factors for the quality models of sewer networks in order to give supportive knowledge for both model developing and applying. Conceptual quality models which were proposed previously by the authors, with total suspended solid (TSS) as quality indicator, are used in this study. 2) Clustering algorithm is used for exploratory analysis. Further analysis about the correlations between different factors and model performance is also carried out. 3) All the study and analysis are demonstrated on a real pilot based on Louis Fargue urban catchment in Bordeaux. Conclusive results about the influencing factors, flow rate, rain intensity and pipe length, as well as their correlations with the TSS models are elaborated.Peer ReviewedPostprint (author's final draft