A Comparison Between Machine Learning Techniques to Find Leaks in Pipe Networks PDF Download
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Author: Joseph Cornelius Van der Walt
Publisher:
ISBN:
Category :
Languages : en
Pages : 216
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Book Description
In 2012, the National Non-Revenue Water assessment revealed that South Africa has 37% of non-revenue water. With the steadily growing demand for this scarce resource, the detection of leaks in pipe networks is becoming more important. Currently, in South Africa the primary method of detecting leaks is to install pressure management systems and monitoring minimum night time ows [1]. The pressure- ow deviation method, can be used to formulate an inverse analysis model based leak detection problem. This problem can then be solved using Arti cial Neural Networks, Support Vector Machines and other optimization methods. With EPANET, di erent networks were tested to compare these methods to nding leaks, using an inverse analysis formulated problem. Four di erent numerical networks were modeled and tested, a simple single pipe network, a small agricultural site, a distribution network proposed and investigated by Poulakis et al. [2] and the simulated model of the experimental network that was designed and commissioned during the study in our laboratory. From the numerical investigation, it was found that the optimization methods struggled to nd solutions for simple networks with in nite number of solutions for the problem. For more complex numerical networks, it was seen that the Support Vector machine and the Arti cial Neural Networks trained to the averages of their respective data sets. Errors to ensure an accurate solution found by these algorithms were calculated as 2:6% for the numerical experimental network. The experimental network consisted of six possible leaking pipes, each having a length of 3m and a diameter of 10mm. Three leak cases were tested with diameters of 3mm and 2mm. Overall, the Support Vector machine could locate the leaking pipe with the best accuracy, while the minimizing of non-regularized error could calculate the size and location of the leak the most accurately. Multiple leak cases were measured with the experimental network. The Support Vector machine was tested on these measurements, where it was found that two of the three leak cases could be solved with relative accuracies. Sensor usage optimization was completed on the measurements for the experimental network, where it was found that the leaks could be classi ed correctly with probabilities higher than 98% if only two sensors were used in the training of the SVM instead of all twelve. Overall this method of leak detection shows promise for certain applications in the future. With practical applications on water distribution, transportation, and agricultural networks.
Author: Joseph Cornelius Van der Walt
Publisher:
ISBN:
Category :
Languages : en
Pages : 216
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Book Description
In 2012, the National Non-Revenue Water assessment revealed that South Africa has 37% of non-revenue water. With the steadily growing demand for this scarce resource, the detection of leaks in pipe networks is becoming more important. Currently, in South Africa the primary method of detecting leaks is to install pressure management systems and monitoring minimum night time ows [1]. The pressure- ow deviation method, can be used to formulate an inverse analysis model based leak detection problem. This problem can then be solved using Arti cial Neural Networks, Support Vector Machines and other optimization methods. With EPANET, di erent networks were tested to compare these methods to nding leaks, using an inverse analysis formulated problem. Four di erent numerical networks were modeled and tested, a simple single pipe network, a small agricultural site, a distribution network proposed and investigated by Poulakis et al. [2] and the simulated model of the experimental network that was designed and commissioned during the study in our laboratory. From the numerical investigation, it was found that the optimization methods struggled to nd solutions for simple networks with in nite number of solutions for the problem. For more complex numerical networks, it was seen that the Support Vector machine and the Arti cial Neural Networks trained to the averages of their respective data sets. Errors to ensure an accurate solution found by these algorithms were calculated as 2:6% for the numerical experimental network. The experimental network consisted of six possible leaking pipes, each having a length of 3m and a diameter of 10mm. Three leak cases were tested with diameters of 3mm and 2mm. Overall, the Support Vector machine could locate the leaking pipe with the best accuracy, while the minimizing of non-regularized error could calculate the size and location of the leak the most accurately. Multiple leak cases were measured with the experimental network. The Support Vector machine was tested on these measurements, where it was found that two of the three leak cases could be solved with relative accuracies. Sensor usage optimization was completed on the measurements for the experimental network, where it was found that the leaks could be classi ed correctly with probabilities higher than 98% if only two sensors were used in the training of the SVM instead of all twelve. Overall this method of leak detection shows promise for certain applications in the future. With practical applications on water distribution, transportation, and agricultural networks.
Author: Stuart Hamilton
Publisher: IWA Publishing
ISBN: 1780404700
Category : Science
Languages : en
Pages : 106
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Book Description
Ageing infrastructure and declining water resources are major concerns with a growing global population. Controlling water loss has therefore become a priority for water utilities around the world. In order to improve efficiencies, water utilities need to apply good practices in leak detection. Leak Detection: Technology and Implementation assists water utilities with the development and implementation of leak detection programs. Leak detection and repair is one of the components of controlling water loss. In addition, techniques are discussed within this book and relevant case studies are presented. This book provides useful and practical information on leakage issues.
Author: Samer El-Zahab
Publisher:
ISBN:
Category :
Languages : en
Pages : 238
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Book Description
Today, 844 million humans around the world have no access to safe drinking water. Furthermore, every 90 seconds, one child dies from water-related illnesses. Major cities lose 15% - 50% of their water and, in some cases, losses may reach up to 70%, mostly due to leaks. Therefore, it is paramount to preserve water as an invaluable resource through water networks, particularly in large cities in which leak repair may cause disruption. Municipalities usually tackle leak problems using various detection systems and technologies, often long after leaks occur; however, such efforts are not enough to detect leaks at early stages. Therefore, the main objectives of the present research are to develop and validate a leak detection system and to optimize leak repair prioritization. The development of the leak detection models goes through several phases: (1) technology and device selection, (2) experimental work, (3) signal analysis, (4) selection of parameters, (5) machine learning model development and (6) validation of developed models. To detect leaks, vibration signals are collected through a variety of controlled experiments on PVC and ductile iron pipelines using wireless accelerometers, i.e., micro-electronic mechanical sensors (MEMS). The signals are analyzed to pinpoint leaks in water pipelines. Similarly, acoustic signals are collected from a pilot project in the city of Montreal, using noise loggers as another detection technology. The collected signals are also analyzed to detect and pinpoint the leaks. The leak detection system has presented promising results using both technologies. The developed MEMS model is capable of accurately pinpointing leaks within 12 centimeters from the exact location. Comparatively, for noise loggers, the developed model can detect the exact leak location within a 25-cm radius for an actual leak. The leak repair prioritization model uses two optimization techniques: (1) a well-known genetic algorithm and (2) a newly innovative Lazy Serpent Algorithm that is developed in the present research. The Lazy Serpent Algorithm has proved capable of surpassing the genetic algorithm in determining a more optimal schedule using much less computation time. The developed research proves that automated real-time leak detection is possible and can help governments save water resource and funds. The developed research proves the viability of accelerometers as a standalone leak detection technology and opens the door for further research and experimentations. The leak detection system model helps municipalities and water resource agencies rapidly detect leaks when they occur in real-time. The developed pinpointing models facilitate the leak repair process by precisely determine the leak location where the repair works should be conducted. The Lazy Serpent Algorithm helps municipalities better distribute their resources to maximize their desired benefits.
Author: Amit Kumar
Publisher: Springer Nature
ISBN: 981157961X
Category : Technology & Engineering
Languages : en
Pages : 1561
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Book Description
This book is a collection of research papers and articles presented at the 3rd International Conference on Communications and Cyber-Physical Engineering (ICCCE 2020), held on 1-2 February 2020 at CMR Engineering College, Hyderabad, Telangana, India. Discussing the latest developments in voice and data communication engineering, cyber-physical systems, network science, communication software, image and multimedia processing research and applications, as well as communication technologies and other related technologies, it includes contributions from both academia and industry. This book is a valuable resource for scientists, research scholars and PG students working to formulate their research ideas and find the future directions in these areas. Further, it may serve as a reference work to understand the latest engineering and technologies used by practicing engineers in the field of communication engineering.
Author: Hemachandran K
Publisher: CRC Press
ISBN: 1000569586
Category : Business & Economics
Languages : en
Pages : 147
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Book Description
This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.
Author: M. Farley
Publisher: IWA Publishing
ISBN: 1900222116
Category : Science
Languages : en
Pages : 297
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Book Description
This is a best practice manual for addressing water losses in water distribution networks worldwide. Systems and methodologies are presented for improving water loss and leakage management in a range of networks, from systems with a well-developed infrastructure to those in developing countries where the network may need to be upgraded. The key feature of the manual is a diagnostic approach to develop a water loss strategy - using the appropriate tools to find the right solutions - which can be applied to any network. The methods of assessing the scale and volume of water loss are outlined, together with the procedures for setting up leakage monitoring and detection systems. As well as real losses (leakage) procedures for addressing apparent losses, by introducing regulatory and customer metering policies are explained. Suggestions are made for demand management and water conservation programmes, to complement the water loss strategy. Recommendations are made for training workshops and operation and maintenance programmes to ensure skills transfer and sustainability. The manual is illustrated throughout with case studies. Losses in Water Distribution Networks will appeal to a wide range of practitioners responsible for designing and managing a water loss strategy. These include consultants, operations managers, engineers, technicians and operational staff. It will also be a valuable reference for senior managers and decision makers, who may require an overview of the principles and procedures for controlling losses. The book will also be suitable as a source document for courses in Water Engineering, Resource Management and Environmental Management.
Author: J. N. Reddy
Publisher: Springer Nature
ISBN: 9811933030
Category : Architecture
Languages : en
Pages : 1023
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Book Description
This book presents articles from the Second International Conference on Sustainable Civil Engineering and Architecture, held on 30 October 2021 in Ho Chi Minh City, Vietnam. The conference brings together international experts from both academia and industry to share their knowledge, expertise, to facilitate collaboration and improve cooperation in the field. The book highlights the latest advances in sustainable architecture and civil engineering, covering topics such as offshore structures, structural engineering, construction materials, and architecture.
Author: Panagiotis Tsakalides
Publisher: CRC Press
ISBN: 1351986171
Category : Technology & Engineering
Languages : en
Pages : 348
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Book Description
The effects of climate change, rapid urbanization, and aging infrastructure challenge water policymakers to confront a radical paradigm shift in water resources utilization. Recent advances in sensing, networking, processing, and control have provided the means for sustainable solutions in water management, and their implementation in water infrastructures is collectively referred to as "smart water grids." Smart water grids depend upon cyber-physical system principles to effectively respond to issues regarding the scalability and reliability of dynamic and inaccessible environments. As such, unique smart water grid issues associated with front-end signal processing, communication, control, and data analysis must be jointly addressed, while sophisticated techniques for data analytics must be introduced into cyber-physical systems research. This book provides a thorough description of the best practices for designing and implementing cyber-physical systems that are tailored to different aspects of smart water grids. It is organized into three distinct, yet complementary areas, namely: the theory behind water-oriented cyber-physical systems with an emphasis on front-end sensing and processing, communication technologies, and learning techniques over water data; the applications and emerging topics of cyber-physical systems for water urban infrastructures, including real-life deployments, modern control tools, and economic aspects for smart water grids; and the applications and emerging topics across natural environments, emphasizing the evolution of fresh water resources. The structured discussion yields a rich, comprehensive body of knowledge on this emerging topic of research and engineering. As water issues intensify on a global scale, this book offers an algorithmic and practical toolkit for intermediate and advanced readers as well as professionals and researchers who are active in, or interested in, learning more about smart water grids. Key Features: Emphasizes the multidisciplinary nature of this emerging topic, covering both theoretical and practical aspects of this area while providing insights on existing deployments, which can serve as design examples for new applications. Explores how modern signal processing and machine learning techniques can contribute and enrich the potential of smart water grids, well beyond conventional closed-loop control techniques. Highlights complementary aspects that will help shape the future of smart water grids, such as consumption awareness, economic aspects, and control tools in industrial water treatment as well as the impact of climate change on fresh water resources. Enables the reader to better understand this emerging topic, investing in current state-of-the-art and future technological roadmaps for smart water grids.
Author: Thomas Brox
Publisher: Springer
ISBN: 303012939X
Category : Computers
Languages : en
Pages : 717
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Book Description
This book constitutes the refereed proceedings of the 40th German Conference on Pattern Recognition, GCPR 2018, held in Stuttgart, Germany, in October 2018. The 48 revised full papers presented were carefully reviewed and selected from 118 submissions. The German Conference on Pattern Recognition is the annual symposium of the German Association for Pattern Recognition (DAGM). It is the national venue for recent advances in image processing, pattern recognition, and computer vision and it follows the long tradition of the DAGM conference series, which has been renamed to GCPR in 2013 to reflect its increasing internationalization. In 2018 in Stuttgart, the conference series celebrated its 40th anniversary.
Author: Ranko Stojan Pudar
Publisher:
ISBN:
Category :
Languages : en
Pages : 206
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Book Description
