Smart Water Grid Research Articles

Smart Water Management Grid

The Smart Water Management Grid (SWMG) lever- age the power of the Internet of Things (IoT) to revolutionize water management, ensuring efficiency, sustainability, and reliability in water distribution systems. By integrating sensors such as level monitoring, pressure monitoring, leakage control, and pH sensors, these grids enable real-time monitoring and intelligent decision-making to optimize water usage and minimize wastage. One crucial aspect of smart water grids is level monitoring, which tracks water levels in reservoirs, tanks, and pipelines. By continuously monitoring water levels, authorities can ensure optimal storage and distribution, preventing shortages or over- flows. Pressure monitoring sensors further enhance efficiency by maintaining optimal pressure throughout the distribution network, minimizing energy consumption and reducing the risk of pipe bursts. Leakage control is another significant feature facilitated by IoT sensors in smart water grids. Advanced leak detection sensors can pinpoint even minor leaks in the system, enabling swift repairs and preventing significant water losses. This proactive approach not only conserves water but also reduces operational costs and mitigates potential damage to infrastructure and the environment. Moreover, integrating pH sensors into the water grid allows for real-time monitoring of water quality. Maintaining proper pH levels is critical for ensuring safe and potable water supply. By detecting variations in pH levels, authorities can promptly identify and address potential contamination issues, safeguarding public health and preventing waterborne diseases.

EXPLORING THEORETICAL CONSTRUCTS OF URBAN RESILIENCE THROUGH SMART WATER GRIDS: CASE STUDIES IN AFRICAN AND U.S. CITIES

This review paper explores the theoretical constructs of urban resilience through the lens of smart water grids, focusing on comparative insights between African and U.S. cities. Urban resilience, a critical concern in the face of climate change and urbanization, encompasses the capacity of urban systems to absorb, adapt, and recover from various shocks and stresses. Integrating advanced technologies such as sensors, smart meters, and data analytics, smart water grids emerge as innovative solutions to enhance urban resilience through improved water management. This paper synthesizes the literature on the intersections between urban resilience and smart water grids, highlighting theoretical constructs that emerge from integrating smart water technologies with urban resilience strategies. It discusses the implications of these constructs for urban planning and policy-making, considering socioeconomic, technological, and governance dimensions, and reflects on the importance of contextual factors. The paper identifies gaps in current theoretical and empirical understanding, suggesting areas for future research, including comparative studies and interdisciplinary approaches. By providing a comprehensive overview of how smart water grids contribute to urban resilience, this paper contributes to the broader discourse on sustainable urban development and the role of technology in addressing contemporary urban challenges.
 Keywords: Urban Resilience, Smart Water Grids, Climate Change Adaptation, Sustainable Urban Development.

Machine learning applications for anomaly detection in Smart Water Metering Networks: A systematic review

The digitization of the water sector has led to the emergence of Smart Water Metering Networks (SWMNs), which enable automated and continuous water consumption measurement. However, challenges persist in efficiently managing and transmitting the vast amount of data generated by these networks. To address this, researchers have proposed anomaly detection techniques to identify and detect anomalies within SWMNs. In the realm of anomaly detection, a substantial body of research has emerged. However, there is a notable gap in the literature on a comprehensive synthesis of Machine Learning (ML) applications for anomaly detection in SWMNs. To bridge this knowledge gap, this study evaluated thirty-two research papers written between 2016 and 2023, focusing on ML applications for anomaly detection in smart water systems, smart water grids, water distribution networks, and water networks. From an initial pool of 725 research papers, those directly related to ML-based anomaly detection techniques were selected and analysed using both quantitative and qualitative data analysis. The study revealed that ML techniques such as k-Nearest Neighbors (kNN), Autoencoders, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Random Forest, Decision Trees, and Support Vector Machines (SVM) have made significant contributions to anomaly detection within these networks. Notably, researchers frequently employed multiple ML techniques to enhance accuracy. Performance metrics analysis demonstrated that F1 score, precision, accuracy, and recall were commonly used to assess the quality of ML anomaly detection techniques. This review aims to provide researchers with recommendations for selecting suitable ML anomaly detection techniques in SWMNs. The findings underscore the promising nature of ML applications for anomaly detection in SWMNs and highlight the need for further research in this area.

Demonstration of real-time monitoring in smart graded-water supply grid: an institutional case study

Abstract Real-time information on water supply and quality is a crucial asset for planning and managing water resources, infrastructure, and scientific research for sustainable development. In this direction, the innovative concept of smart water infrastructure is progressing. The present paper reports a case study on the demonstration of a `smart graded-water supply grid' on the campus of the Indian Institute of Technology Jodhpur, India. The paper describes the transformation of ∼13 km long water distribution network that supplies drinking water to ∼5,000 inhabitants into smart supply grid by deploying sensors and establishing an IoT-enabled real-time monitoring platform. The data sets of water flow and pressure collected from sensor nodes are analyzed to understand the characteristic diurnal water usage profiles unique to student hostels on the campus. The data show a distinctive consumption profile of student hostels over the weekdays with a maximum peak consumption of 16.38 m3/h. Monitoring of vital quality parameters such as chlorine, pH, and temperature demonstrate acceptable levels thereby ensuring compliance with safety standards. The purpose of the paper is to provide insights from a real-world case and close the knowledge gap between general awareness and the potential of smart water grid in sustainable management of graded-water services.

From Radio to In-Pipe Acoustic Communication for Smart Water Networks in Urban Environments: Design Challenges and Future Trends

The smart management of water resources is an increasingly important topic in today’s society. In this context, the paradigm of Smart Water Grids (SWGs) aims at a constant monitoring through a network of smart nodes deployed over the water distribution infrastructure. This facilitates a continuous assessment of water quality and the state of health of the pipeline infrastructure, enabling early detection of leaks and water contamination. Acoustic-wave-based technology has arisen as a viable communication technique among the nodes of the network. Such technology can be suitable for replacing traditional wireless networks in SWGs, as the acoustic channel is intrinsically embedded in the water supply network. However, the fluid-filled pipe is one of the most challenging media for data communication. Existing works proposing in-pipe acoustic communication systems are promising, but a comparison between the different implementations and their performance has not yet been reported. This paper reviews existing works dealing with acoustic-based communication networks in real large-scale urban water supply networks. For this purpose, an overview of the characteristics, trends and design challenges of existing works is provided in the present work as a guideline for future research.

Pressure-Based Demand Aggregation and Calibration of Normal and Abnormal Diurnal Patterns for Smart Water Grid in Near Real-Time

Pressure-Based Demand Aggregation and Calibration of Normal and Abnormal Diurnal Patterns for Smart Water Grid in Near Real-Time

Analysis of the effects of COVID-19 restriction policies on drinking water consumption by smart water network data filtering

Abstract People's habits changed during the COVID-19 pandemic and the consequent containment policies, with numerous implications in all fields. In particular, restrictions had important consequences for drinking water consumption. The present work analyses this influence in the Soccavo district of Naples (Campania), in Italy, during the two periods of strongest restrictions in 2020: the national Lockdown (March 11–May 3) and the autumn Red Zone (November 16–December 6). A large amount of data, referred to single-household flowmeters connected to a Smart Water Grid acquisition system, was collected for the years 2019 (considered the average reference year) and 2020. The first step was the preliminary filtering of the data, by identification and elimination of anomalies and outliers, as well as anomalous annual patterns, through clustering and classification. The second step consisted of the comparison of the same meters in two consecutive years considering the daily and weekly average hourly patterns, the average daily patterns of midweek days, Saturdays, and Sundays, respectively, and the total daily volumes. The results are consistent with those in the literature. Some general trends in literature data were sought and pointed out in the present paper.

Application of the concept of smart city and smart water management for the new capital city

The Indonesian government has decided to move the State Capital (IKN) from DKI Jakarta to East Kalimantan Province. One of IKN’s visions is to build a smart, modern city that meets international standards. This paper examines the concept of smart city and smart water management (SWM) that will be applied to the new IKNs to build an integrated, sophisticated and sustainable clean water supply system. The method used was analyzing concepts related to smart cities and SWM applications in the world and Indonesia. Based on the results of the analysis of the abovementioned concepts, several conclusions were obtained, namely that an SWM support system is indispensable in water system management, leak detection, and water quality monitoring. Then, a smart water grid is used to develop control over water resources, infrastructure, and water discharge with high efficiency and sustainability. The flood early warning system must be utilized to minimize losses or victims caused by flood disasters. In addition, an online water quality monitoring system is needed to monitor water quality online and in real time. The integrated implementation of the smart city and SWM concepts described in the paper is expected to produce safe, healthy, and sustainable drinking water services.

Smart Water Grids and Digital Twin for the Management of System Efficiency in Water Distribution Networks

One of the main factors contributing to water scarcity is water loss in water distribution systems, which mainly arises from a lack of adequate knowledge in the design process, optimization of water availability, and poor maintenance/management of the system. Thus, from the perspective of sustainable and integrated management of water resources, it is essential to enhance system efficiency by monitoring existing system elements and enhancing network maintenance/management practices. The current study establishes a smart water grid (SWG) with a digital twin (DT) for a water infrastructure to improve monitoring, management, and system efficiency. Such a tool allows live monitoring of system components, which can analyze different scenarios and variables, such as pressures, operating devices, regulation of different valves, and head-loss factors. The current study explores a case study in which local constraints amplify significant water losses. It develops and examines the DT model’s application in the Gaula water distribution network (WDN) in Madeira Island, Portugal. The developed methodology resulted in a significant potential reduction in real water losses, which presented a huge value of 434,273 m3 (~80%) and significantly improved system efficiency. The result shows a meaningful economic benefit, with savings of about EUR 165k in water loss volume with limiting pressures above the regulatory maximum of 60 m w.c. after the district metered area (DMA) sectorization and the requalification of the network. Hence, only 40% of the total annual volume, concerning the status quo situation, is necessary to supply the demand. The infrastructure leakage index measures the existing real losses and the reduction potential, reaching a value of 21.15, much higher than the recommended value of 4, revealing the great potential for improving the system efficiency using the proposed methodology.

High Fidelity Digital Twin-Based Anomaly Detection and Localization for Smart Water Grid Operation Management

Smart Water Grid (SWG) plays a critical role in sustaining cities economic and social development, but challenges remain in fully realizing the benefits of SWG. While Digital twin (DT) has been discussed in some literature for possible SWG applications, there has been limited, or no technical framework developed to facilitate SWG operation and management. In this paper, a generic framework is developed for constructing SWG high fidelity Digital Twin (DT) by integrating digital thread with various digital models for visualization, data-driven analysis, physics-based simulations, and decision-making support. Both the physics-based models and data-driven models are trained/retrained and calibrated/re-calibrated respectively by using the data collected with the sensors installed throughout a SWG. The information derived from the SWG DT can be diagnostic, predictive, and prescriptive to significantly augment users’ intelligence for improving SWG operation and management. One important application of digital twin augmented intelligence is illustrated to timely detect and localize anomaly events, which may include, but not be limited to, pipe bursts and unauthorized water usages. The solution is tested on the selected areas in Singapore to construct the ever-green DT by calibrating and recalibrating the models for near real-time SWG operation management. The case study was conducted for three supply zones with the total pipeline length of more than 1000 km and 40 weeks of monitoring data, collected by 89 pressure monitoring stations and 8 flow meters at inlets and boundary. More than 3300 data-driven models are trained for optimizing the model performance to achieve accuracy of greater than 80% F1 score for detecting anomaly events, which are subsequently localized within 400 m with 2–3 days lead time.

1-D convolution neural network based leak detection, location and size estimation in smart water grid

ABSTRACT Water is one of the essential natural resources for survival, but the water transportation system faces significant challenges because of huge water loss due to leaky pipeline systems. An IoT based novel SWG prototype has been developed and reported in this work. The SWG comprises sensors and devices that can continuously and remotely monitor the pressure, temperature, flow, pH, turbidity, etc., of the water being transported. Moreover, a novel 1-D CNN model has been developed by creating an artificial leak on the pipeline that takes input data points as a chunk of 5-minute time series to the network and gives output in leak detection, location and size estimation simultaneously. Further, the developed model is compared with other state of the art machine learning techniques and the proposed model is found better in terms of accuracy which is 94.32%, 91.91% and 89.85% for leak detection, size estimation and location respectively.

기후변화 대응을 위한 디지털 물 관리 기술의 적용

As abnormal climates occur frequently around the world, the frequency and intensity of natural disasters are increased. In this study, trends of natural disasters are analyzed and identified the applied technology of smart water grid and digital water management, which are the latest water management systems to respond to climate change. Water-related damages such as typhoons and floods accounted for a large proportion of natural disasters around the world. In the case of Asia, it was found that over the past 50 years, typhoons caused the largest casualties of human life, and the biggest economic loss was caused by floods. In the case of Korea, the biggest economic loss over the past 10 years was disasters caused by floods and typhoons. In order to minimize the damage caused by these disasters, digital water resource technology is being applied worldwide. In this study, application cases of smart water management and digital technologies were investigated. In addition, the application direction of digital water management technology was proposed.

IoT-Based Smart Water Management Systems for Residential Buildings in Saudi Arabia

Water is a precious resource that can be intelligently managed. Effective water usage demands computerized home water supply management in a culture where water tanks, motors, and pumps are ubiquitous. Water management is crucial for the government and the citizens in countries like Saudi Arabia. The issue is providing a constant, high-quality, low-cost water supply. This study introduces a smart water management (IoT-SWM) system that may be used in structures that do not have access to a constant water supply but instead have water stored in enormous tanks underneath. The GSM module collects water use data from each home in a community and transmits it to the cloud, where it is analyzed. A smart water grid is a hybrid application that uses an inspection mode to identify leaks and measure the resulting height differences to keep track of the tank’s water level. The system automatically deactivates the affected section after detecting any water shortage or malfunction in the system mechanism, such as broken valves, pumps, or pipes. It sends an emergency signal to building managers. It monitors essential water quality elements regularly, and if they fall below acceptable levels, it sends warning signals to the building management, who can take action. Over an extended period, the system monitored and recorded all water quality metrics. The system restarts when the water pump has been reconnected and sends an emergency alert. As a result, the suggested system has been an excellent replacement for Saudi Arabia’s mechanically operated system.

Predictive modeling framework accelerated by GPU computing for smart water grid data-driven analysis in near real-time

With the increase adoption of monitoring technology for Smart Water Grid (SWG) system, accurate prediction of SWG status is essential for water companies to effectively operate and manage water networks. Although different data-driven predictive techniques have been developed over last two decades with various degree of success, predictive modeling is not widely adopted in practice. The challenges remain in (1) developing accurate and robust model for near real-time applications; (2) the selection of training data size, model update frequency, and input data size for competent model performance. Therefore, in this paper, a versatile framework is developed by integrating data preprocessing procedures with various statistical methods, machine learning, and deep learning algorithms. It is flexible and accelerated by the latest graphics processing unit computing technology. The case study using the real-world monitoring data shows that the prediction accuracy of 91% and 98% has been achieved for flow and pressures, respectively.

A smart water grid network for water supply management systems

This paper proposes a smart water grids network (SWGN) architecture that combines the advantages of fog computing, internet of things (IoT), long range wide area network (LoRaWAN), and Software-defined networking (SDN). The main aims of the SWG architecture are to optimize data routing and monitor water supply and quality in real-time. SWGN handles a vast amount of data that is collected by IoT devices from different points related to water supply and quality. The data is processed in a distributed way by a number of fog servers that are located at the edge of the network. The fog controllers are deployed at the fog layer in order to take action locally for frequent events. The cloud layer has a cloud controller to take actions globally for infrequent events. The LoRaWAN provides communication technology that allows devices to operate regularly. The SDN technology decouples network traffic to control data routing decisions efficiently. A primitive evaluation under the Mininet emulator, focusing on SDN, shows the feasibility and efficiency of the architecture.

Daily Model Calibration with Water Loss Estimation and Localization Using Continuous Monitoring Data in Water Distribution Networks

Due to the increasing deployment of sensors in water distribution networks (WDNs) for continuous monitoring, hydrodynamic data are readily available for engineers to improve the daily operations of WDNs. In collaboration with Public Utility Board (PUB), Singapore’s National Water Agency, an alternative model calibration approach using continuously monitored data is proposed to facilitate PUB’s smart water grid (SWG). The generic approach was developed as an integrated solution methodology for practical engineers to conduct a series of systematic analyses daily, namely, (1) estimating the system’s daily nonrevenue water (NRW) volume and NRW time series; (2) adjusting the available pumps’ operational curves and control statuses, followed by calibrating the system’s net demand pattern to fulfill the flow balance accounting for the actual consumption; (3) identifying and rectifying possible offsets in the monitored pressure head values for each sensor station; (4) performing model calibration with anomaly localization analysis when the system’s total NRW volume exceeds its assumed background NRW volume; and (5) calibrating other physical properties to fulfill the system’s energy balance, especially for the peak demand hours. The effectiveness of our proposed approach was then tested and verified using a real-world WDN having total pipe length of >650 km with available monitoring data. Key findings from the case study analysis include (1) anomaly events localized including, but not limited to, five out of six reported leaks for the selected week to within 400 m with lead time of 1–2 days; (2) the system’s initial flow imbalance addressed by estimating the daily NRW volume and localizing the possible anomaly events; and (3) pipe roughness values calibrated to further improve the energy balance in the system, especially during the peak demand hours, by attaining an average mean absolute percentage error (MAPE) score of 2.5%.

Improving near real-time anomaly event detection and classification with trend change detection for smart water grid operation management

ABSTRACT To improve the efficiency of Smart Water Grid (SWG) operation, it is of great importance to timely detect and classify anomaly events. Authors have developed a comprehensive solution framework for anomaly detection. However, the framework may detect undesirable false alarms, which will incur wasteful field investigation. Therefore, it is imperative to reduce the number of false alarms for real world SWG operation. In this paper, the anomaly event classification has been improved by an algorithm of trend change detection in two phases including (1) detecting trend change in single sensor, and (2) identifying the effective evaluation time window by correlating the flow anomaly trend period and pressure anomaly trend periods to filter the detected events. A real case study has been conducted to demonstrate the application of the proposed algorithm; the number of false alarms is reduced by as much as 31% without compromising the detection accuracy.

Need for Upgradation of Technologies in Water Supply Projects in India

Abstract: India, water supply services in urban and rural areas in India are provided mainly by public authorities. These authorities are mostly involved in operation and maintenance of the water supply schemes. When any additional fund becomes available, expansion of pipe network, additional house connections and repair works are taken up. But, there is an urgent need for upgradation of technologies in the water supply schemes. At present, there is not much effort for upgradation of technologies for treatment of water, measurement and control of system, increase in efficiency of pumping stations and water distribution network, etc. The author highlights some of the feasible technologies, which may be evaluated and adopted by water authorities. For technology upgradation, enormous works are needed in India, which in turn will necessitate research and generate employment and business opportunities to Industries. Keywords: Upgradation of Technology, Water Treatment, Water Quality Assessment, Smart Water Meters, Water Quality Sensor, Smart Water Grid.

Public Perceptions of Household IoT Smart Water “Event” Meters in the UK—Implications for Urban Water Governance

Cities around the world are facing water availability challenges, intensified by increasing populations and climate change. Technology, such as household smart meters measuring domestic water consumption, can play a role in demand management, yet a deeper understanding of public expectations and the practicalities of city-wide implementation is required. This article explores public perceptions of smart water meters that use Internet of Things (IoT) technology and machine learning to profile household water use “events” and anomalies. By leveraging insights from an online survey implemented in the UK (n = 558), this article explores factors influencing the likelihood of citizens choosing to have this type of meter installed along with potential societal barriers and opportunities. Nearly half of the participants said they would choose to have such a meter installed and logistic regression showed predictive variables were younger ages, being male, those with existing water meters and those with other smart devices. The likelihood of choosing this type of water meter was also associated with preferences to have control over data privacy, whether the meter would reduce water bills and whether it was provided free of charge. We locate these results within other contemporary experiences of smart meters and water grids in urban contexts to discuss practical challenges of using real-time environmental data for urban water governance. Policymakers and water resources planners should continue to monitor public perceptions, implement urban experiments and cost-benefit analyses to better interpret the wider benefits of such technology for behavioral and educational interventions within a more digitized and increasingly data-centric water grid.

Smart water grid technology based on deep learning: a review

In recent years, the development of deep learning technology has made breakthroughs in computer vision, natural language processing and other fields. The Smart Water Grid (SWG) technology based on deep learning has also been a hot area of research in recent years. It has achieved better performance in the related detection and prediction of urban pipe networks. Therefore, this survey paper presents an extensive review of the application of deep learning to several different issues related to the SWG. This paper emphasises feasibility studies and summarises the state-of-the-art development in this field from a technical point of view, which consists of pipeline leakage and burst detection, contamination source identification and water demand forecasting. Furthermore, this paper also proposes challenges and future directions in these key research areas, demonstrating that deep learning-based SWG technology is still an emerging and encouraging research field.