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

A survey of machine learning methods applied to anomaly detection on drinking-water quality data

Abstract: Traditional machine learning (ML) techniques such as support vector machine, logistic regression, and artificial neural network have been applied most frequently in water quality anomaly detection tasks. This paper presents a review of progress and advances made in detecting anomalies in water quality data using ML techniques. The review encompasses both traditional ML and deep learning (DL) approaches. Our findings indicate that: 1) Generally, DL approaches outperform traditional ML techniques in terms of feature learning accuracy and fewer false positive rates. However, is difficult to make a fair comparison between studies because of different datasets, models and parameters employed. 2) We notice that despite advances made and the advantages of the extreme learning machine (ELM), application of ELM is sparsely exploited in this domain. This study also proposes a hybrid DL-ELM framework as a possible solution that could be investigated further and used to detect anomalies in water quality data

Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms

The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management

Developing Multi-Scale Models for Water Quality Management in Drinking Water Distribution Systems

Drinking water supply systems belong to the group of critical infrastructure systems that support the socioeconomic development of our modern societies. In addition, drinking water infrastructure plays a key role in the protection of public health by providing a common access to clean and safe water for all our municipal, industrial, and firefighting purposes. Yet, in the United States, much of our national water infrastructure is now approaching the end of its useful life while investments in its replacement and rehabilitation have been consistently inadequate. Furthermore, the aging water infrastructure has often been operated empirically, and the embracement of modern technologies in infrastructure monitoring and management has been limited. Deterioration of the water infrastructure and poor water quality management practices both have serious impacts on public health due to the increased likelihood of contamination events and waterborne disease outbreaks. Water quality reaching the consumers’ taps is largely dependent on a group of physical, chemical, and biological interactions that take place as the water transports through the pipes of the distribution system and inside premise plumbing. These interactions include the decay of disinfectant residuals, the formation of disinfection by-products (DBPs), the corrosion of pipe materials, and the growth and accumulation of microbial species. In addition, the highly dynamic nature of the system’s hydraulics adds another layer of complexity as they control the fate and transport of the various constituents. On the other hand, the huge scale of water distribution systems contributes dramatically to this deterioration mainly due to the long transport times between treatment and consumption points. Hence, utilities face a considerable challenge to efficiently manage the water quality in their aging distribution systems, and to stay in compliance with all regulatory standards. By integrating on-line monitoring with real-time simulation and control, smart water networks offer a promising paradigm shift to the way utilities manage water quality in their systems. Yet, multiple scientific gaps and engineering challenges still stand in the way towards the successful implementation of such advanced systems. In general, a fundamental understanding of the different physical, chemical, and biological processes that control the water quality is a crucial first step towards developing useful modeling tools. Furthermore, water quality models need to be accurate; to properly simulate the concentrations of the different constituents at the points of consumption, and fast; to allow their implementation in real-time optimization algorithms that sample different operational scenarios in real-time. On-line water quality monitoring tools need be both reliable and inexpensive to enable the ubiquitous surveillance of the system at all times. The main objective of this dissertation is to create advanced computational tools for water quality management in water distribution systems through the development and application of a multi-scale modeling framework. Since the above-mentioned interactions take place at different length and time scales, this work aims at developing computational models that are capable of providing the best description of each of the processes of interest by properly simulating each of its underlying phenomena at its appropriate scale of resolution. Molecular scale modeling using tools of ab-initio quantum chemical calculations and molecular dynamics simulations is employed to provide detailed descriptions of the chemical reactions happening at the atomistic level with the aim of investigating reaction mechanisms and developing novel materials for environmental sensing. Continuum scale reactive-transport models are developed for simulating the spatial and temporal distributions of the different compounds at the pipe level considering the effects of the dynamic hydraulics in the system driven by the spatiotemporal variability in water demands. System scale models are designed to optimize the operation of the different elements of the system by performing large-scale simulations coupled with optimization algorithms to identify the optimal operational strategies as a basis for accurate decision-making and superior water quality management. In conclusion, the computational models developed in this study can either be implemented as stand-alone tools for simulating the fundamental processes dictating the water quality at different scales of resolution, or be integrated into a unified framework in which information from the small scale models are propagated into the larger scale models to render a high fidelity representation of these processes

IoT-based smart water network management challenges and future trend

Abstract: In recent years, internet of things (IoT)-based tech- nology has been deployed to different areas of water supply system. This is in line with the Smart Water Network Manage- ment (SWNM) initiative, that aims to propose a methodology to improve operational performance and monitoring of water supply systems. In the smart water network management applications, IoT-based technology is facing quite a few challenges. In this paper, we present IoT’s applications for monitoring the water supply network with emphasis on its application to water quality and leakage monitoring. In these applications, the technical challenges of IoT-based technology are also discussed. Finally, future research directions in this domain are also highlighted, which may be useful for further studies

Hydrolink 2020/4. Artificial intelligent

Topic: Artificial Intelligenc

Rule-based Decision Support System For Sensor Deployment In Drinking Water Networks

Drinking water distribution systems are inherently vulnerable to malicious contaminant events with environmental health concerns such as total trihalomethanes (TTHMs), lead, and chlorine residual. In response to the needs for long-term monitoring, one of the most significant challenges currently facing the water industry is to investigate the sensor placement strategies with modern concepts of and approaches to risk management. This study develops a Rule-based Decision Support System (RBDSS) to generate sensor deployment strategies with no computational burden as we oftentimes encountered via large-scale optimization analyses. Three rules were derived to address the efficacy and efficiency characteristics and they include: 1) intensity, 2) accessibility, and 3) complexity rules. To retrieve the information of population exposure, the well-calibrated EPANET model was applied for the purpose of demonstration of vulnerability assessment. Graph theory was applied to retrieve the implication of complexity rule eliminating the need to deal with temporal variability. In case study 1, implementation potential was assessed by using a small-scale drinking water network in rural Kentucky, the United States with the sensitivity analysis. The RBDSS was also applied to two networks, a small-scale and large-scale network, in “The Battle of the Water Sensor Network” (BWSN) in order to compare its performances with the other models. In case study 2, the RBDSS has been modified by implementing four objective indexes, the expected time of detection (Z1), the expected population affected prior to detection (Z2), the expected consumption of contaminant water prior to detection, and the detection likelihood (Z4), are being used to evaluate RBDSS’s performance and compare to other models in Network 1 analysis in BWSN. Lastly, the implementation of iv weighted optimization is applied to the large water distribution analysis in case study 3, Network 2 in BWSN

Optimal sensor placement for sewer capacity risk management

2019 Spring.Includes bibliographical references.Complex linear assets, such as those found in transportation and utilities, are vital to economies, and in some cases, to public health. Wastewater collection systems in the United States are vital to both. Yet effective approaches to remediating failures in these systems remains an unresolved shortfall for system operators. This shortfall is evident in the estimated 850 billion gallons of untreated sewage that escapes combined sewer pipes each year (US EPA 2004a) and the estimated 40,000 sanitary sewer overflows and 400,000 backups of untreated sewage into basements (US EPA 2001). Failures in wastewater collection systems can be prevented if they can be detected in time to apply intervention strategies such as pipe maintenance, repair, or rehabilitation. This is the essence of a risk management process. The International Council on Systems Engineering recommends that risks be prioritized as a function of severity and occurrence and that criteria be established for acceptable and unacceptable risks (INCOSE 2007). A significant impediment to applying generally accepted risk models to wastewater collection systems is the difficulty of quantifying risk likelihoods. These difficulties stem from the size and complexity of the systems, the lack of data and statistics characterizing the distribution of risk, the high cost of evaluating even a small number of components, and the lack of methods to quantify risk. This research investigates new methods to assess risk likelihood of failure through a novel approach to placement of sensors in wastewater collection systems. The hypothesis is that iterative movement of water level sensors, directed by a specialized metaheuristic search technique, can improve the efficiency of discovering locations of unacceptable risk. An agent-based simulation is constructed to validate the performance of this technique along with testing its sensitivity to varying environments. The results demonstrated that a multi-phase search strategy, with a varying number of sensors deployed in each phase, could efficiently discover locations of unacceptable risk that could be managed via a perpetual monitoring, analysis, and remediation process. A number of promising well-defined future research opportunities also emerged from the performance of this research