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

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.

Physical integrity of medical exam gloves with repeated applications of disinfecting agents: evidence for extended use

Background: The COVID-19 pandemic has created global shortages of personal protective equipment (PPE) such as medical exam gloves, forcing healthcare workers to either forgo or reuse PPE to keep themselves and patients safe from infection. In severely resource-constrained situations, limited cycles of disinfection and extended use of gloves is recommended by the U.S. Centers for Disease Control and Prevention (CDC) to conserve supplies. However, these guidelines are based on limited evidence. Methods: Serial cycles of hand hygiene were performed on gloved hands using alcohol-based hand rub (ABHR) (six and ten cycles), 0.1% sodium hypochlorite (bleach) solution (ten cycles), or soap and water (ten cycles) on three types of latex and three types of nitrile medical exam gloves, purchased in the United States and India. A modified FDA-approved water-leak test was performed to evaluate glove integrity after repeated applications of these disinfecting agents. 80 gloves per disinfectant-glove type combination were tested. Within each glove type the proportion of gloves that failed the water-leak test for each disinfectant was compared to that of the control using a non-inferiority design with a non-inferiority margin of five percentage points. Results were also aggregated by glove material, and combined for overall results. Findings: When aggregated by glove material, the dilute bleach exposure demonstrated the lowest difference in proportion failed between treatment and control arms: -2.5 percentage points (95% CI: -5.3 to 0.3) for nitrile, 0.6 percentage points (95% CI: -2.6 to 3.8) for non-powdered latex. For US-purchased gloves tested with six and ten applications of ABHR, the mean difference in failure risk between treatment and control gloves was within the prespecified non-inferiority margin of five percentage points or less, though some findings were inconclusive because confidence intervals extended beyond the non-inferiority margin. The aggregated difference in failure risk between treatment and control gloves was 3.5 percentage points (0.6 to 6.4) for soap and water, and 2.3 percentage points (-0.5 to 5.0) and 5.0 percentage points (1.8 to 8.2) for 10 and 6 applications of ABHR, respectively. The majority of leaks occurred in the interdigital webs (35%) and on the fingers (34%). Conclusion: Current guidelines do not recommend extended use of a single-use PPE under normal supply conditions. However, our findings indicate that some combinations of glove types and disinfection methods may allow for extended use under crisis conditions. We found that ten applications of dilute bleach solution have the least impact on glove integrity, compared to repeated applications of ABHR and soap and water. However, the majority of glove and exposure combinations were inconclusive with respect to non-inferiority with a 5 percentage point non-inferiority margin. Testing specific glove and disinfectant combinations may be worthwhile for settings facing glove shortages during which extended use is necessary. The modified water-leak testing method used here is a low-resource method that could easily be reproduced in different contexts.

Water leak detection based on convolutional neural network using actual leak sounds and the hold-out method

The main purpose of this study was to investigate whether machine learning can be used to detect leak sounds in the field. A method for detecting water leaks was developed using a convolutional neural network (CNN), after taking recurrence plots and visualising the time series as input data. In collaboration with a pipeline restoration company, 20 acoustic datasets of leak sounds were recorded by sensors at 10 leak sites. The detection ability of the constructed CNN model was tested using the hold-out method for the 20 cases: 19 showed more than 70% accuracy, of which 15 showed more than 80%.

Proposal of a Big data System for an Intelligent Management of Water Resources

Today, advanced technologies like Big Data, IoT, and Cloud Computing can provide new opportunities and applications in all sectors. In the water sector, water scarcity has become a common concern of different institutions and actors worldwide. In this context, several approaches and systems have been proposed and developed, using these technologies, allowing intelligent water resources management. Internet of Things can be used for assisting the Water Industry to collect data, manage and monitor the water infrastructures using smart devices. Big Data is a strategic technology for analyzing and interpreting collected data into valuable and helpful information for better decision making. This paper presents Big Data and Internet of Things technologies. It addresses theirs uses in some use cases such as municipal water losses, water pollution in agriculture, water Leak detection, etc., to provide new systems and innovative solutions for intelligent water resources management. Based on this study, we propose a Big Data and IoT architecture for intelligent water resources management.