Smart Water Research Articles

ICT Innovation to Promote Sustainable Development Goals: Implementation of Smart Water Pipeline Monitoring System Based on Narrowband Internet of Things

ICT Innovation to Promote Sustainable Development Goals: Implementation of Smart Water Pipeline Monitoring System Based on Narrowband Internet of Things

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.

Digital twin water conservancy early warning system based on physical level and numerical simulation in intelligent water conservancy construction

Abstract. In 2023, the Central Committee of the Communist Party of China and The State Council issued the Overall Layout Plan for the Construction of Digital China, emphasizing the construction of a smart water conservancy system with digital twin basins as the core. The Guangxi Zhuang Autonomous Region government has clearly proposed to actively build a "digital twin Pinglu Canal", and attaches great importance to the construction of digital twin platform application services during the construction of the Western land-Sea New Passage (Pinglu) canal. Aiming at the problems such as unsatisfactory early warning effect, inaccurate data and imprecise model of existing smart water conservancy technology, this paper proposes a digital twin system for disaster early warning based on physical level and numerical simulation technology, establishes a visual digital water conservancy model and digital twin smart water conservancy cloud platform, and combines multi-physics field and multi-dimensional coupling algorithm to respond to technical pain points. To achieve scientific and accurate monitoring and intelligent management, and contribute to the construction of digitally empowered Pinglu Canal.

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)

Spatiotemporal monitoring of groundwater supply and active energy for irrigation practice in semi-arid regions of Tunisia with machine learning

ABSTRACT Semiarid regions are facing overexploitation of groundwater resources to meet irrigation needs. Monitoring the water-energy nexus allows for optimal management of extracted water volumes and consumed energy. A semiarid zone was selected in the Nabeul region of Tunisia where 14 farmers, whose wells were equipped with smart electricity and water meters (SWEMs), for instant monitoring of pumped water volumes and the electrical energy required for irrigation. Monthly data over a period of 8 months were used to study the variations in water volumes and active energy. The analysis of variance classified farmers into four groups based on water volumes and five groups based on active energy. Spatial variability analysis using kriging showed that the northeast zone is the most solicited in terms of water pumping and energy consumption with water volume exceeding 4,000 m3/month and active energy reaching 2,500 kWh/month. The prediction of energy based on water volume using machine learning techniques such as random forest and support vector machine was successfully conducted. The tools generated by the methodology were applied to a chosen case in the region to estimate active energy and validate the results obtained. The implemented framework allows for better management of groundwater resources for irrigation.

Hydraulic parameter estimation for district heating based on laboratory experiments

In this paper we consider calibration of hydraulic models for district heating networks based on operational data. We extend previous theoretical work on the topic to handle real-world complications, namely unknown valve characteristics and hysteresis. We generate two datasets in the Smart Water Infrastructure Laboratory in Aalborg, Denmark, on which we evaluate the proposed procedure. In the first data set the system is controlled in such a way to excite all operational modes in terms of combinations of valve set-points. Here the best performing model predicted volume flow rates within roughly 5 and 10 % deviation from the mean volume flow rate for the consumer with the highest and lowest mean volume flow rates respectively. This performance was met in the majority of the operational region. In the second data set, the system was controlled in order to mimic real load curves. The model trained on this data set performed similarly well when evaluated on data in the operational range represented in the training data. However, the model performance deteriorated when evaluated on data which was not represented in the training data.

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.

Three-dimensional convolutional neural network for leak detection and localization in smart water distribution systems

Smart meters such as advanced metering infrastructure (AMI) can significantly improve identifying realistic sized leaks in water distribution networks (WDNs). However, to date, detection/localization methods for AMI systems are extremely limited. In this study, to examine the benefits of using AMIs for leak detection within distribution network, a three-dimensional (3D) convolutional neural network (CNN) deep learning (DL) model is proposed that can account for temporally and spatially distributed information of pressures. The 3D CNN is tested for a real WDN in Austin using the realistic sized leaks (e.g., 3 L/s for 150-mm pipes) that are generated from hydraulic simulations. The model's performance is evaluated using detection probability, false alarm rate, and localization pipe distance metrics. In addition, the strength of using DL for leak identification is examined by comparing the CNN results with those from an optimization-based model. The 3D CNN performed better than the optimization model indicating that DL has advantages over conventional tools such as optimization methods. However, its adaptability may limit its use in some cases. Since DL can be significantly impacted by hydraulic simulation model, a way to handle modelling error must be determined. In addition, as network changes occur, retraining is required that may be time consuming and have difficulty with the number of failure conditions.

Assessment of Water Consumption Behavior in Single Households Using Smart Water Meters

Smart meters monitor hourly water consumption patterns while reducing the cost and inconvenience of traditional meters. This study comprehensively analyzes 1871 households that previously used traditional meters from the distribution point to the distribution area. All the households were equipped with smart meters and the data collected were used for analysis. On the basis of the total estimated water consumption, 227 households were classified as single households. These households were further classified into single-worker and -nonworker households. This study analyzed smart meter data to evaluate the timings and amounts of water consumption peaks. The results indicate that worker households peaked at 8:00, with 29 L/h of consumption on weekdays, and peaked again on evenings at 20:00–21:00, averaging 32 L/h. For nonworker households, the peak occurred at 9:00, with 20 L/h, with no major changes in the afternoon, and a second peak occurred at 19:00–20:00 in evening, with an average of 19 L/h. Moreover, worker households peaked at 8:00 and 20:00 on weekdays, and at 9:00 and 19:00 on weekends. It was revealed that worker households consume 10% more on weekends than on weekdays, and 262% more from 13:00 to 16:00. These findings may assist in water supply planning by supporting distribution schedules on the basis of peak household consumption, leading to more helpful resource management.

Experimental study of the effect of oil polarity on smart waterflooding in carbonate reservoirs

This study investigates the influence of oil polarity on interfacial tension (IFT), contact angle, oil recovery, and effluent pH in smart water and Low-salinity water injection. The results indicate that the interaction between the hydration shell of ions and the polar components (PCs) of oil is crucial. Increasing oil polarity enhances the potential for interaction with the hydration shell of ions, leading to reduced IFT, altered wettability, and improved oil recovery; which could be boosted by the contribution of a higher number of anions in the smart water bulk through the enhancement of their interaction with the PCs (especially acidic components) of oil. The study demonstrates that increasing the SO42− concentration in seawater increased oil recovery for oils with higher acid component content, as indicated by total acid number values of 0.87, 0.99, and 1.32 mgKOH/g, the tertiary oil recovery factors for these oils were 61.10%, 69.82%, and 87.09%, respectively. The effluent pH results align with the findings of contact angle and oil recovery, confirming the dominant influence of anions on oil recovery. The interaction between the PCs of oil and the hydration shell of ions is thus highlighted as a critical factor in the observed outcomes.

Requirements for water data ecosystems: results from a business ecosystem case study

This paper studies the factors that affect the emergence of water data ecosystems using a case study as a research method. The study is based on interviews conducted with partners in a comprehensive business ecosystem focused on the development of smart water network management. Eleven representatives from six private companies, the waterworks of a city, and three organizations that provide water supply management services for municipalities were interviewed. The paper presents analysis of the interview results focusing on the interviewees’ thoughts on the state of water data systems in Finland and on the factors that affect the emergence of water data ecosystems in Finland.The interview results indicate a clear need for water data ecosystems but also obstacles preventing their emergence. Inadequate understanding on the part of customer, a lack of water data, regulations, and underdeveloped agreements were seen to hinder the development of water data solutions. In addition to ecosystem development, the emergence of water data ecosystems requires investment and the development of water data solutions, solution concepts, and demonstrations to show the value of the ecosystem. The results show that ecosystems need a clear rationale and vision, effective management of water data sharing, and mechanisms to ensure the scalability of water data ecosystems.

Machine learning regression-based prediction model for the autonomous control of coagulant dosing in smart water purification plants

ABSTRACT The global issue of water scarcity is escalating due to urbanization and increased demand. This paper proposes a machine learning (ML) regression-based model for automatic coagulant dosing control in smart water purification plants (SWPPs).1 The model uses random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), and k-nearest neighbors (KNN) algorithms. Performance metrics include MAE, MSE, RMSE, MAPE, and R2. The RF algorithm showed superior performance, with MAE of 0.005, MSE of 0.002, RMSE of 0.05, and MAPE of 0.000 for anion-poly aluminum chloride dosing, and MAE of 0.007, MSE of 0.00, RMSE of 0.02, and MAPE of 0.000 for Polymax dosing. The RF model's performance is due to its robust handling of large datasets and ensemble learning approach. Limitations include testing only two coagulants and reliance on historical data. These findings suggest integrating advanced water management, energy systems, and facility management to make SWPPs feasible and efficient, establishing a foundation for future applications of ML in chemical processes.

Assessment of CROPWAT 8.0 model accuracy under deficit irrigation scheduling: A sustainable path toward smart water supply

ABSTRACT As the world confronts mounting challenges to water security, efficient irrigation becomes ever more critical. Escalating water scarcity challenges necessitate innovative smart irrigation solutions. This study investigates the potential of integrating deficit irrigation (DI) with the CROPWAT 8.0 model for sustainable and water-productive irrigation. This research evaluated the model's accuracy in generating DI schedules at 70% field capacity (FC) (T1) compared to schedules derived from frequency domain reflectometry (FDR) soil moisture sensors at 70% FC (T2). The results demonstrated economic viability and strong accuracy for T1 with consistently lower values of mean absolute error (3.6165 and 4.3987) and root mean square error (4.4447 and 5.5055) while compared to T2 across two years. In addition, T1 achieved significant water savings (43.47 and 45.16%) alongside higher water productivity (8.11 and 8.22 kg m−3), respectively, for both the years compared to full irrigation (FI). These findings highlight CROPWAT 8.0 model–driven DI scheduling as a promising smart irrigation approach for tackling water scarcity, offering economic benefits to farmers while promoting water security.

A Smart Water Bottle and Companion App (HidrateSpark 3) to Improve Bladder-Filling Compliance in Patients With Prostate Cancer Receiving Radiotherapy: Nonrandomized Trial of Feasibility and Acceptability.

Patients with prostate cancer undergoing radiation therapy (RT) need comfortably full bladders to reduce toxicities during treatment. Poor compliance is common with standard of care written or verbal instructions, leading to wasted patient value (PV) and clinic resources via poor throughput efficiency (TE). Herein, we assessed the feasibility and acceptability of a smartphone-based behavioral intervention (SBI) to improve bladder-filling compliance and methods for quantifying PV and TE. In total, 36 patients with prostate cancer were enrolled in a single-institution, closed-access, nonrandomized feasibility trial. The SBI consists of a fully automated smart water bottle and smartphone app. Both pieces alert the patient to empty his bladder and drink a personalized volume goal, based on simulation bladder volume, 1.25 hours before his scheduled RT. Patients were trained to adjust their volume goal and notification times to achieve comfortably full bladders. The primary end point was met if qualitative (QLC) and quantitative compliance (QNC) were >80%. For QLC, patients were asked if they prepared their bladders before daily RT. QNC was met if bladder volumes on daily cone-beam tomography were >75% of the simulation's volume. The Service User Technology Acceptability Questionnaire (SUTAQ) was given in person pre- and post-SBI. Additional acceptability and engagement end points were met if >3 out of 5 across 4 domains on the SUTAQ and >80% (15/18) of patients used the device >50% of the time, respectively. Finally, the impact of SBI on PV and TE was measured by time spent in a clinic and on the linear accelerator (linac), respectively, and contrasted with matched controls. QLC was 100% in 375 out of 398 (94.2%) total treatments, while QNC was 88.9% in 341 out of 398 (85.7%) total treatments. Of a total score of 5, patients scored 4.33 on privacy concerns, 4 on belief in benefits, 4.56 on satisfaction, and 4.24 on usability via SUTAQ. Further, 83% (15/18) of patients used the SBI on >50% of treatments. Patients in the intervention arm spent less time in a clinic (53.24, SEM 1.71 minutes) compared to the control (75.01, SEM 2.26 minutes) group (P<.001). Similarly, the intervention arm spent less time on the linac (10.67, SEM 0.40 minutes) compared to the control (14.19, SEM 0.32 minutes) group (P<.001). This digital intervention trial showed high rates of bladder-filling compliance and engagement. High patient value and TE were feasibly quantified by shortened clinic times and linac usage, respectively. Future studies are needed to evaluate clinical outcomes, patient experience, and cost-benefit. ClinicalTrials.gov NCT04946214; https://www.clinicaltrials.gov/study/NCT04946214.

Overview of the Research Status of Intelligent Water Conservancy Technology System

A digital twin is a new trend in the development of the current smart water conservancy industry. The main research content of intelligent water conservancy is clarified. This paper first summarizes and combs the relevant system architecture of smart water conservancy, and puts forward a smart water conservancy framework based on digital twins, highlighting the characteristics of virtual and real interaction, and symbiosis of the water conservancy twin platform. Secondly, the status quo of intelligent water conservancy “sky, air, ground and water” integrated monitoring technology, big data and artificial intelligence, model platform technology, knowledge graph and security technology is analyzed. From the perspective of application, the research progress of each technology in water security, water resources and hydraulic engineering is reviewed. Although the construction of smart water conservancy has made remarkable progress, it still faces many challenges such as data governance, technology integration and innovation, and standardization. In view of these challenges, this paper puts forward a series of countermeasures, and looks forward to the future development direction of intelligent water conservancy.

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

중국 1인 거주자의 생활환경을 고려한 스마트 정수기 디자인에 관한 연구 - 사용자 경험의 심층 분석을 중심으로

This study explores the design of intelligent water dispensers for young rental housing groups to meet their needs for convenience, instant water supply, and energy efficiency. With the acceleration of urbanization and the increase in youth mobility, traditional drinking water solutions can no longer fully meet the needs of this group. Through literature research, questionnaire surveys, and user and expert interviews, this article comprehensively analyzes the market demand, user preferences, and user experience of smart water dispensers. Research has found that young users prefer smart water dispensers that are hot, energy-efficient, and easy to operate, while also having high expectations for product appearance design and technological innovation. Based on the research results, this article proposes a user centered design strategy, aiming to improve user experience and enhance product market competitiveness by optimizing the design elements of intelligent water dispensers at the instinctive, behavioral, and reflective levels. This study provides theoretical support and practical guidance for the product innovation and market strategy of intelligent water dispensers.

Theoretical and experimental investigation of the impact of oil functional groups on the performance of smart water in clay-rich sandstones

This research investigated the effect of ion concentration on the performance of low salinity water under different conditions. First, the effect of injection water composition on interparticle forces in quartz-kaolinite, kaolinite-kaolinite, and quartz-oil complexes was tested and modeled. The study used two oil samples, one with a high total acid number (TAN) and the other with a low TAN. The results illustrated that reducing the concentration of divalent ions to 10 mM resulted in the electric double layer (EDL) around the clay and quartz particles and the high TAN oil droplets, expanding and intensifying the repulsive forces. Next, the study investigated the effect of injection water composition and formation oil type on wettability and oil/water interfacial tension (IFT). The results were consistent with the modeling of interparticle forces. Reducing the divalent cation concentration to 10 mM led to IFT reduction and wettability alteration in high TAN oil, but low TAN oil reacted less to this change, with the contact angle and IFT remaining almost constant. Sandpack flooding experiments demonstrated that reducing the concentration of divalent cations incremented the recovery factor (RF) in the presence of high TAN oil. However, the RF increment was minimal for the low TAN oil sample. Finally, different low salinity water scenarios were injected into sandpacks containing migrating fines. By comparing the results of high TAN oil and low TAN oil samples, the study observed that fine migration was more effective than wettability alteration and IFT reduction mechanisms for increasing the RF of sandstone reservoirs.