Acquisition of Optimal Operational Strategies for Water Distribution Systems Using Evolutionary Algorithms, Machine Learning and Preference Ordering Routine

2008 ◽  
Author(s):  
Ivaltemir (Tinil) Barros Carrijo ◽  
Luisa Fernanda Ribeiro Reis
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithms ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Preference Ordering ◽  
Operational Strategies

scholarly journals Linking discolouration modelling and biofilm behaviour within drinking water distribution systems

2016 ◽  
Vol 16 (4) ◽  
pp. 942-950 ◽  
Author(s):  
S. Husband ◽  
K. E. Fish ◽  
I. Douterelo ◽  
J. Boxall
Keyword(s):  
Drinking Water ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particulate Material ◽  
Operational Strategies ◽  
Drinking Water Distribution Systems ◽  
Cohesive Properties ◽  
Common Process ◽  
Drinking Water Distribution

High quality drinking water exits modern treatment works, yet water quality degradation such as discolouration continues to occur within drinking water distribution systems (DWDS). Discolouration is observed globally, suggesting a common process despite variations in source, treatment, disinfection and network configurations. The primary cause of discolouration has been identified as mobilisation of particulate material from pipe walls and the verified Prediction of Discolouration in Distribution Systems (PODDS) model uses measurable network hydraulics to simulate this response. In this paper the cohesive properties of discolouration material are explored and it is hypothesised that in simulating the turbidity response, the PODDS model is actually describing the development and cohesive strength behaviour of biofilms. Applying this concept can therefore facilitate a rapid and simple assessment of DWDS biofilm activity. A review of the findings from PODDS studies conducted internationally is presented, focussing on the macro or observable aspects of discolouration. These are compared and contrasted with associated biofilm studies which consider discolouration material at the micro-scale. Combining the results from these (past) studies to improve the understanding of interactions between microbial ecology and discolouration are discussed with a view to DWDS operational strategies that safeguard and optimise drinking water supply.

scholarly journals Experiments based comparative evaluations of machine learning techniques for leak detection in water distribution systems

2021 ◽  
Author(s):  
Maryam Kammoun ◽  
Amina Kammoun ◽  
Mohamed Abid
Keyword(s):  
Machine Learning ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Leak Detection ◽  
Distribution Networks ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Flow Data ◽  
Learning Techniques

Abstract Leakage in water distribution systems is a significant long-standing problem due to the huge economic and ecological losses. Different leak detection studies have been examined in literature using different types of technologies and data. Currently, although machine learning techniques have achieved tremendous progress in outlier detection approaches, they are still limited in terms of water leak detection applications. This research aims to improve the leak detection performances by refining the choices of learning data and techniques. From this perspective, commonly used techniques for leak detection are assessed in this paper, and the characteristics of hydraulic data are investigated. Four intelligent algorithms are compared, namely k-nearest neighbors, support vector machines, logistic regression, and multi-layer perceptron. This study focuses on six experiments based on identifying outliers in various packages of pressure and flow data, yearly data, seasonal data, night data, and flow data difference to detect leakage in water distribution networks. Different scenarios of realistic water demand in two networks from the benchmark dataset LeakDB are used. Results demonstrate that the leak detection accuracy varies between 30% and 100% depending on the experiment and the choices of algorithms and data.

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

Smart Cities ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 1293-1315
Author(s):  
Neda Mashhadi ◽  
Isam Shahrour ◽  
Nivine Attoue ◽  
Jamal El Khattabi ◽  
Ammar Aljer
Keyword(s):  
Machine Learning ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Economic Losses ◽  
Water Leakage ◽  
Water Leak ◽  
Leak Localization ◽  
Detection And Localization ◽  
Selection Of

This paper presents an investigation of the capacity of machine learning methods (ML) to localize leakage in water distribution systems (WDS). This issue is critical because water leakage causes economic losses, damages to the surrounding infrastructures, and soil contamination. Progress in real-time monitoring of WDS and ML has created new opportunities to develop data-based methods for water leak localization. However, the managers of WDS need recommendations for the selection of the appropriate ML methods as well their practical use for leakage localization. This paper contributes to this issue through an investigation of the capacity of ML methods to localize leakage in WDS. The campus of Lille University was used as support for this research. The paper is presented as follows: First, flow and pressure data were determined using EPANET software; then, the generated data were used to investigate the capacity of six ML methods to localize water leakage. Finally, the results of the investigations were used for leakage localization from offline water flow data. The results showed excellent performance for leakage localization by the artificial neural network, logistic regression, and random forest, but there were low performances for the unsupervised methods because of overlapping clusters.

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