Smart Water Management Research Articles

Using computational fluid dynamics and deep learning for leak detection and localization in a smart water management system

Water leakages in distribution systems offer significant challenges, resulting in water loss, infrastructure damage, and environmental hazards. As urban populations grow and water resources become scarcer, water management systems’ efficiency becomes increasingly important. Traditional leak detection methods are inadequate and labour-intensive, making them unsuitable for large-scale infrastructure. In addition, limitations in the data representing pipes of varying diameters and materials under various leak scenarios leads to complexity in automatic water leak detection and localization. In response to this challenge, this research effort combines Computational Fluid Dynamics (CFD) and Deep Learning (DL) to enhance leak detection and localization. The study simulates water flow in pipes of varying diameters and materials under various leak scenarios using CFD. Consequentially deep learning models like the Bi-Layered ReLU Encoder + Softmax and Tri-Layered ReLU Encoder + Softmax are trained and tested. The results indicate improvements in detection accuracy by 2.4% and localization by 5.6%, highlighting the potential of this hybrid approach to enhance the reliability and efficiency of water management systems, ultimately promoting water conservation and reducing infrastructure damage.

Expediting decarbonization in energy, waste, and water sector through digitalization in sustainable smart cities (SSC): Case-studies in Malaysia and China based on Industry 5.0 paradigm

This work explores the application of Industry 5.0 principles in smart cities development in Malaysia and China, focusing on digital transformation for sustainable urban development. The study presents case-studies from both the countries, highlighting the implementation strategies, challenges, and outcomes associated with integrating advanced technologies to enhance efficiency, climate resilience, and sustainability. This study aims to develop a data-driven methodology to address the absence of region-specific frameworks for sustainable smart cities (SSCs) and to evaluate their impacts. In Malaysia, the implementation of smart energy management systems that utilize IoT and AI has shown promise in reducing carbon footprints and maximizing resource efficiency. China's focus on smart water management using sensor networks and real-time data analytics provides insights into effective water conservation. Smart waste management systems have increased recycling rates by 20-30%. Progress is crucial for the region's pursuit of SSCs to reach a global investment of USD 2.5 trillion by 2025. This work concludes by discussing the implications of this work in both countries to achieve sustainable urbanization through Industry 5.0 technologies. This work offers recommendations for policymakers, urban planners, and technologists to navigate the complexities of smart city development, while providing a roadmap to leverage digital transformation to achieve decarbonization goals in energy, waste and water sector by 2060.

Leak detection and leak localization in a smart water management system using computational fluid dynamics (CFD) and deep learning (DL)

Leak detection and leak localization in a smart water management system using computational fluid dynamics (CFD) and deep learning (DL)

Smart Building Digital Twin for Interior Water Distribution System Management

Abstract. A smart campus integrates the Internet of Things, artificial intelligence, and real-time sensor data to optimize campus functions and create a context-aware decision-support platform for effective campus management. A crucial aspect of a smart campus is the water distribution system, which faces several challenges due to bursts, leaks, and water quality issues. This study uses Digital Twin technology to address these challenges through real-time monitoring, detection, and management of campus water distribution networks. A building-level digital twin is developed for the interior water networks of the Daphne Cockwell Complex at Toronto Metropolitan University. The major components include a 3D network model capable of analysis and simulations, a smart water management system, and real-time visualization. A 3D static model of the interior water utility network is created using the Industrial Foundation Class data. The semantic and topological models based on graph models are developed for network analysis. The models are integrated into the ESRI ArcGIS Pro to facilitate BIM-GIS integration. A dynamic model is created by incorporating automatic meter readings based on IoT. Combined with the 3D model of the utility network, the digital twin monitors and visualizes building water consumption, facilitating anomaly detection and real-time maintenance by the facilities management department. This study provides practical guidance for managing water distribution systems in university buildings, with potential applications in other complex environments. Future work aims to develop a system for detecting complex event processes by integrating different utilities.

Water Leak Detection: A Comprehensive Review of Methods, Challenges, and Future Directions

This paper provides a comprehensive review of the methods and techniques developed for detecting leaks in water distribution systems, with a focus on highlighting their strengths, weaknesses, and areas for future research. Given the substantial economic, social, and environmental impacts of undetected leaks, timely detection and precise location of leaks are critical concerns for water authorities. This review categorizes existing methods into traditional approaches, such as manual sounding, and modern techniques involving smart water management and sensor technologies. A multidimensional bibliometric analysis was employed to systematically identify, select, and evaluate 600 scholarly articles on water leak detection, sourced from the Scopus database over a 23-year period (2000–2023). The paper evaluates each method based on leak sensitivity, burst detection, continuous monitoring, alarm accuracy, and implementation costs. Novel insights include an analysis of emerging smart water technologies and their integration into real-world water distribution networks, offering improved efficiency in leak detection. The paper also identifies key gaps in current research and suggests future directions for advancing the accuracy and cost-effectiveness of these technologies.

An Effective Smart Water Quality Monitoring and Management System Using IoT and Machine Learning

An Effective Smart Water Quality Monitoring and Management System Using IoT and Machine Learning

Leveraging Cost-Effective AI and Smart Technologies for Rapid Infrastructural Development in USA

High cost of building makes houses expensive for US citizens and residents. Thus, this study proposes the leveraging of cost-effective artificial intelligence (AI) and smart technologies (ST) for rapid infrastructural development in US. It considers them as sustainable means of tackling the challenges for the attainment of affordable houses. The study explores the potentials of prominent AI and smart technologies capable of reducing the cost of building houses in the US, for which houses would become affordable for all. The primary data are obtained from telephone interviews with 10 construction workers and 5 experts of AI, alongside observation and introspection. The secondary data are drawn from library and the internet. Qualitative method, thematic and content analyses, systematic review, and descriptive and interpretive tools are employed. The results show Machine Learning, Natural Language Processing, Computer Vision, Reinforcement Learning, and Robotic Process Automation to be prominent cost-effective AI technologies, while Building Automation Systems, Internet of Things, Renewable Energy Systems, and Smart Water Management Systems are cost-effective smart technologies. The study concludes that the identified AI and smart technologies are not only cost-effective, but also transformative and innovation-driven and can be leveraged to increase efficiency, productivity, quality delivery and satisfactory services. The study recommends them to government and organizations for cost-effectiveness towards attaining rapid infrastructural development in the USA.

Island Water Management Cyprus Facts Finding Mission and Gap Identifications

Water management on islands faces unique challenges due to limited size, isolation, climate change impacts, and economic development needs. These factors intensify water scarcity, highlighting the need for sustainable resource development through multidisciplinary approaches. Cyprus, the third-largest Mediterranean island, exemplifies these challenges, with the lowest water availability per capita in the region and severe water scarcity exacerbated by climate change. Addressing these issues requires a comprehensive master plan that integrates technical, environmental, social, and economic factors, focusing on climate change impacts on the water cycle. The Energy, Environment, and Water Research Center (EEWRC) plays a pivotal role in global resource management, emphasizing sustainable development goals and Integrated Water Resources Management Policy. EEWRC’s key initiatives include hydrodiplomacy and WEF Nexus and Integrated Water Resource Management IWRM, monitoring sediment transport pollution, water quality monitoring of basins and aquifers, smart water management of conventional and non-conventional water, and nature-based solutions for water and land management integration. To improve water management in Cyprus, establishing a National Commission for Water Experts is recommended. This commission would provide a detailed roadmap for water management, aligning with Sustainable Development Goals (SDGs) and the Climate Change Initiative coordinated by the Cyprus Institute. Additionally, organizing a conference focused on “Cyprus Island Water Management” would help launch this concept. In conclusion, it is crucial to develop a long-term strategy for optimal water resource management in Cyprus, addressing water issues, promoting conservation, launching climate-resilient initiatives, and fostering collaboration with national and international entities for success.

Agriculture 4.0: Polymer Hydrogels as Delivery Agents of Active Ingredients.

The evolution from conventional to modern agricultural practices, characterized by Agriculture 4.0 principles such as the application of innovative materials, smart water, and nutrition management, addresses the present-day challenges of food supply. In this context, polymer hydrogels have become a promising material for enhancing agricultural productivity due to their ability to retain and then release water, which can help alleviate the need for frequent irrigation in dryland environments. Furthermore, the controlled release of fertilizers by the hydrogels decreases chemical overdosing risks and the environmental impact associated with the use of agrochemicals. The potential of polymer hydrogels in sustainable agriculture and farming and their impact on soil quality is revealed by their ability to deliver nutritional and protective active ingredients. Thus, the impact of hydrogels on plant growth, development, and yield was discussed. The question of which hydrogels are more suitable for agriculture-natural or synthetic-is debatable, as both have their merits and drawbacks. An analysis of polymer hydrogel life cycles in terms of their initial material has shown the advantage of bio-based hydrogels, such as cellulose, lignin, starch, alginate, chitosan, and their derivatives and hybrids, aligning with sustainable practices and reducing dependence on non-renewable resources.

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.

Water park of the future: Monarch butterfly slide with eco-friendly design and smart water management

In this study, an innovative water park concept slide that aims to keep water consumption low was designed and produced. Traditional water parks are out of use at a rate of approximately 65% because they are inefficient in water and energy consumption. This work will be a pioneering step in the sector with the combination of environmentally friendly design, smart water management systems, and sustainable technologies. Users entering the 43% inclined butterfly body will reach a speed of 16 m/sec while performing the vertical swing movement and will move with an acceleration of 0.5m/sec2 on the horizontal axis. Since the high-pressure water coming from the nozzles on the slide will be used only when there is movement, approximately 70% water and 50% energy savings will be achieved and sliding safety will also be increased. The proposed saving system will be produced using the inverter, integrated circuit, smart card, and software developed to be added to the pump and will be adapted to all slides. The fact that this study is a first in the sector will inspire other water parks and lead to the development of projects that consider sustainability and savings issues.

Development of an Integrated Observation Data and Global Numerical Weather Prediction System-Based Rainfall-Runoff Forecasting Model as PJT-I’s Effort in Water Resources Disaster Mitigation

Given the vital role of water resources and their vulnerability to climate-driven disasters, especially floods and droughts, the need for effective disaster mitigation strategies in water resource management is evident. PJT-I, a key player in the industry, grapples with this challenge. To address it, PJT-I has embraced the Smart Water Management System (SWMS) platform and innovative enhancements in rainfall-runoff modeling, showcasing their dedication to bolstering real-time monitoring and predictive capabilities. These advances hold the promise of strengthening operational water resource management, spanning allocation and infrastructure operations, and are particularly relevant in the face of escalating extreme weather events. The study’s focus on the Sengguruh reservoir catchment area serves as an illustration, highlighting the creation of two hydrometric-based models: the AWLR Gadang model and the Sengguruh reservoir model. Their effectiveness is validated through data calibration and integration of GFS forecast data, enabling 120-hour forecasting. Rigorous assessment involving correlation, efficiency, determination, and error metrics provides insights into the models’ predictive capacities and limitations amid the dynamic interplay of rainfall-runoff dynamics.

Unravelling the potential of Big Data-driven decision-making in sustainable water irrigation: An AI perspective

Big Data-driven initiatives have transformed several industries by using large databases to extract vital information and enhance operations. In the field of sustainable water irrigation, Big Data enables intelligent water management, allowing for exact resource allocation, monitoring and analysis. This combination enables smart decision-making, increasing productivity in agriculture while reducing water consumption and environmental impacts. The revolutionary influence of data-driven approaches on managing water resources for sustainable and efficient irrigation practices has significant potential. The research highlights the importance of Big Data-driven approaches in water management by examining their applicability. It explores the artificial intelligence (AI) techniques for water irrigation that is sustainable and determines significant affecting factors efficient water management. The study encourages AI in intelligent water management systems and explains the application of big data in irrigation. It illustrates how smart water management which includes hydrated irrigation and predictive water infrastructure can contribute to AI-driven water management. To handle complicated water supply problems, the research presents an estimated water level model, highlighting the integration of AI and indicating the revolutionary potential of these techniques in attaining sustainable water irrigation. We highlight the novel possibilities of big data-driven methods for sustainable water irrigation, illustrating the essential part of AI techniques in effective water resource allocation.

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices.

Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies' flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors.

Ensemble Empirical Mode Decomposition Granger Causality Test Dynamic Graph Attention Transformer Network: Integrating Transformer and Graph Neural Network Models for Multi-Sensor Cross-Temporal Granularity Water Demand Forecasting

Accurate water demand forecasting is crucial for optimizing the strategies across multiple water sources. This paper proposes the Ensemble Empirical Mode Decomposition Granger causality test Dynamic Graph Attention Transformer Network (EG-DGATN) for multi-sensor cross-temporal granularity water demand forecasting, which combines the Transformer and Graph Neural Networks. It employs the EEMD–Granger test to delineate the interconnections among sensors and extracts the spatiotemporal features within the causal domain by stacking dynamical graph spatiotemporal attention layers. The experimental results demonstrate that compared to baseline models, the EG-DGATN improves the MAPE metrics by 2.12%, 4.33%, and 6.32% in forecasting intervals of 15 min, 45 min, and 90 min, respectively. The model achieves an R2 score of 0.97, indicating outstanding predictive accuracy and exceptional explanatory power for the target variable. This research highlights significant potential applications in predictive tasks within smart water management systems.

Internet of Things (IoT) Enabled Water Distribution System for Smart Water Management

Water is a vital resource for life, and its management is a key issue nowadays the water demand is increasing due to global population growth and urbanization. Present technologies for water control are currently facing interoperability problems due to the lack of support for standardization in monitoring and controlling equipment. This problem affects various processes in water management, such as water consumption, water distribution, system identification, and equipment maintenance. Under these conditions, new technologies in the water management infrastructure have been required to enable the distribution of high-quality water to users safely and cost-effectively, from the perspective of efficiently using our world’s precious water resource. The Internet of Things (IoT) Based Smart Water Management System is a cutting-edge technology that integrates sensors, communication devices, and data analytics to efficiently monitor and manage water resources. This system enables real-time monitoring of water quality, consumption, and distribution, allowing for proactive maintenance and optimization of water usage. By leveraging IoT technology, this smart water management system offers improved accuracy, reliability, and cost-effectiveness in managing water resources. Based on standards, the system proposal is a smart water management system model combining Internet of Things technology with decision support systems. This system is more efficient in distribution monitoring and control approach for a water utility to reduce water loss. This approach will help utility operators to improve water management systems, especially by exploiting emerging technologies.

A REVIEW OF SMART WATER MANAGEMENT SYSTEMS FROM AFRICA AND THE UNITED STATES

This review paper comprehensively examines smart water management systems, focusing on their development, implementation, and impacts within Africa and the United States. It delves into the theoretical frameworks that underlie these systems, including integrating cyber-physical systems, sustainable development principles, and systems theory in water management. The paper further explores the technological innovations driving smart water management, such as the Internet of Things (IoT), Artificial Intelligence (AI), and remote sensing. It discusses the policy and regulatory frameworks that support or hinder these advancements in different regions. Additionally, it assesses the environmental, social, and economic impacts of smart water management, highlighting the benefits of water conservation, improved access to clean water, and efficiency gains. The challenges facing the implementation of smart water management systems are critically analyzed, including technical, financial, and regulatory obstacles. Future directions for smart water management are also discussed, emphasizing the potential for scalability, adaptation, and the importance of cross-sectoral and international collaboration. The paper concludes by underscoring the necessity of integrating technology, policy, and community engagement in smart water management efforts and calls for further research and collaboration to harness the full potential of these systems in addressing global water challenges.
 Keywords: Smart Water Management, Internet of Things (IoT), Artificial Intelligence (AI), Sustainable Development, Policy Frameworks, Water Conservation.

Smart Water Management System Using IOT based Sensors

The advancement of technology in the field of Internet of Things (IoT) have grown rapidly over the past years in the field of water management in various context. The work aims to contribute to general process flow of a smart city management specifically in the field of the residential water management system. As well to design a smart water management system for the residential areas that can be implemented not only to a single residential area but to multiple residential areas. This is achieved through implementing sensor-based water flow meter to replace current mechanical water flow gauge. It is as well addressed the need of a common platform to store data that are currently proposed through an additional data acquisition application. Lastly, the work proposes an affordable prototype to measure water flow rate using IOT based sensors to be implemented at large scale and promising usability testing result.

Smart city: an advanced framework for analyzing public sentiment orientation toward recycled water

The coronavirus pandemic of the past several years has had a profound impact on all aspects of life, including resource utilization. One notable example is the increased demand for freshwater, a lifeblood of our planet, on the other hand, the smart city vision aims to attain a smart water management goal by investing in innovative solutions such as recycled water systems. However, the problem lies in the public’s sentiment and willingness to use this new resource which discourages investors and hinders the development of this field. Therefore, in our work, we applied sentiment analysis using an extended version of the fuzzy logic and neural network model from our previous work, to find out the general public opinion regarding recycled water and to assess the effects of sentiments on the public’s readiness to use this resource. Our analysis was based on a dataset of over 1 million text content from 2013 to 2022. The results show, from spatio-temporal perspectives, that sentiment orientation and acceptance-behavior towards using recycled water have increased positively. Additionally, the public is more concerned in areas driven by the smart city vision than in areas of medium and low economic development, where investment in sensibilization campaigns is needed.

Review of smart water management: IoT and AI in water and wastewater treatment

Integrating the Internet of Things (IoT) and Artificial Intelligence (AI) in smart water management revolutionizes sustainable water resource utilization. This comprehensive review explores these technologies' benefits, challenges, regulatory implications, and future trends. Smart water management enhances operational efficiency, predictive maintenance, and resource conservation while addressing data security and infrastructure investment challenges. Regulatory frameworks play a pivotal role in shaping the responsible deployment of AI and IoT, ensuring data privacy and ethical use. Future trends include advanced sensors, decentralized systems, quantum computing, and blockchain for enhanced water data security. The alignment with Sustainable Development Goals (SDGs) underscores the transformative potential of smart water management in achieving universal access to clean water, climate resilience, and inclusive, sustainable development. As we embrace these technologies, collaboration, public awareness, and ethical considerations will guide the evolution of intelligent and equitable water management systems.