Smart Water Research Articles

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.

Smart Water ATM with Arduino Integration, RFID Authentication, and Dynamic Dispensing for Enhanced Hydration Practices

This proposal outlines the development of a comprehensive solution to address hydration challenges through the creation of a Smart Water ATM with Arduino integration, RFID authentication, and dynamic dispensing capabilities. Traditional water dispensers often fall short in monitoring water intake and promoting optimal hydration practices. In response, our project aims to revolutionize hydration practices by integrating Arduino Mega and Uno boards into a Smart Water ATM with a Bottle Dispenser. This innovative system is designed to dispense specific water quantities based on user preferences, encourage the use of personal water bottles, display water temperature, and operate independently without the need for a water line connection. Prior to prototype fabrication, an extensive literature review and survey were conducted to understand existing water dispenser shortcomings and gather public perspectives in Singapore. The Smart Water ATM addresses identified issues by incorporating sensors to monitor water levels, dispense specific quantities, and measure water temperatures. The prototype fabrication involves designing a stainless-steel housing, 3D printing the Smart Water dispenser unit, and installing tanks, tubing, and electronic components. To enhance user interaction, the Smart Water ATM requires RFID authentication through Identity Cards, tracking daily water consumption. An LCD screen displays the dispensed water volume over the ATM’s lifespan, motivating users to be mindful of their water consumption and minimize wastage. Adjustments will be made for deployment in public spaces, such as train stations, where access to permanent water dispensers is limited. This proposal presents an innovative solution to promote enhanced hydration practices, encouraging users to adopt mindful water consumption habits.

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.

Solar Powered Rain Roofing System For Crops With Smart Watering

Solar Powered Rain Roofing System For Crops With Smart Watering

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.

Examining the relationship between the economic growth, energy use, CO2 emissions, and water resources: Evidence from selected MENA countries

Water shortage and food security concerns are amplified by the spread of irrigated cultures within the context of climatic unpredictability. In the hot, arid MENA area, the frequent drought periods and the continuous deterioration of water resources limits the ecological system's functioning and the sustainable agricultural productivity. Thus, this study aims to evaluate the potential impacts of the evolution of water resources management on the environmental components, using Westerlund’s (2007) panel cointegration and the Common Correlated Effects Mean Group (CCE-MG) estimator for a sample of six MENA economies over 35 years ((1990–2015). In the short run, there is unidirectional causality from agricultureadded value to CO2 and from water productivity to CO2 without any feedback. while, a bidirectional causality nexus was detected between energy consumption and CO2 emission In the long run, however, a feedback causality has been obtained between CO2 emissions, agriculture added value, water productivity, and energy consumption.Sustainable water management, smart water systems, and energy transition in the water related activities are highly recommended as priorities towards a NetZero carbon global economic trend.

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.

Anomalies identification in Smart Water Metering Networks: Fostering improved water efficiency

Smart Water Metering Networks (SWMNs) stand as pivotal infrastructure, crucial for communities and industries. The escalating value of water resources due to climate change and overexploitation underscores the urgency of optimizing these networks for efficiency and resilience. This study focuses on identifying anomalies within SWMNs to address challenges impeding efficient water resource management. Leveraging a comprehensive 72-month dataset from Windhoek, Namibia, this research employs a meticulous analytical approach to unveil diverse anomaly types prevalent within SWMNs. Anomalies, including irregular consumption patterns, leakages, and inaccurate meters, contribute significantly to both apparent and real losses. By scrutinizing this dataset, the study reveals nuanced anomaly patterns like persistent zero consumption and unexpected fluctuations, highlighting the pervasive nature of these issues within the network. The findings not only shed light on these multifaceted anomalies but also lay the groundwork for future advancements in machine learning-based anomaly detection techniques. This research holds promise beyond academia, offering practical implications for water utility management. Identifying and understanding these anomalies serves as a stepping stone toward developing robust detection systems, ultimately fostering heightened efficiency and resilience in water networks. This study serves as a catalyst for strategic improvements, enabling more sustainable and efficient utilization of water resources amidst evolving environmental challenges.

Hydrological time series prediction based on IWOA-ALSTM

The prediction of hydrological time series is of great significance for developing flood and drought prevention approaches and is an important component in research on smart water resources. The nonlinear characteristics of hydrological time series are important factors affecting the accuracy of predictions. To enhance the prediction of the nonlinear component in hydrological time series, we employed an improved whale optimisation algorithm (IWOA) to optimise an attention-based long short-term memory (ALSTM) network. The proposed model is termed IWOA-ALSTM. Specifically, we introduced an attention mechanism between two LSTM layers, enabling adaptive focus on distinct features within each time unit to gather information pertaining to a hydrological time series. Furthermore, given the critical impact of the model hyperparameter configuration on the prediction accuracy and operational efficiency, the proposed improved whale optimisation algorithm facilitates the discovery of optimal hyperparameters for the ALSTM model. In this work, we used nonlinear water level information obtained from Hankou station as experimental data. The results of this model were compared with those of genetic algorithms, particle swarm optimisation algorithms and whale optimisation algorithms. The experiments were conducted using five evaluation metrics, namely, the RMSE, MAE, NSE, SI and DR. The results show that the IWOA is effective at optimising the ALSTM and significantly improves the prediction accuracy of nonlinear hydrological time series.

GIS Constructed Water Monitoring Network System Via IOT

Monitoring the quality of water plays a vital role in the advancement towards intelligent and smart agriculture. It also facilitates the seamless transition to automated monitoring of essential components of human daily needs, as new technologies continue to be developed and adopted in both agricultural and human daily life, particularly in relation to water. Assessing water stress requires accurate measurement of water usage [1]. The objective of this study is to optimize the distribution strategy and achieve more efficient and rapid distribution, while also making better use of technology to address the urgent need for water conservation. One of the criteria for the second filtering phase, which utilized a realtime data collection system, focused on the implementation of water quality monitoring and characterization methods. Water measurement sensors and the Internet of Things (IoT) can provide immediate information on water flow and pressure rates. This information is crucial for the analysis and surveillance of water [2]. At the RCEW campus, online smart water monitoring is imperative to ensure a more efficient and sustainable use of water. By combining the Internet of Things and geographic information systems (GIS), an online smart water monitoring prototype was developed specifically for RCEW [3]. This cutting-edge technology has proven to be highly successful in providing real-time information on water parameters, saving both time and money, while enabling the continuous study of water consumption patterns. This innovative technology seamlessly integrates IoT and GIS for effective water surveillance [4,5].

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.

Development of an IoT-Based Smart Watering System for Monitoring and Increasing Soil Moisture Content in “Tabtim Siam” Pomelo Garden in Pak Phanang District, Nakhon Si Thammarat Province, Southern Thailand

Abstract In Thailand, “Tabtim Siam” pomelo production is essential for domestic consumption and export. Among several environmental factors, soil moisture is the most important factor for citrus plants’ growth and their fruit quality. During the main developmental stages of these plants, the required soil moisture should be above 70% (no water deficit). Therefore, keeping soil moisture at ≥70% in pomelo orchards is crucial. This study developed an IoT-based smart watering system that efficiently monitors soil moisture and assists in maintaining soil moisture at the required level even at a high temperature inside a pomelo orchard in Pak Phanang District, Nakhon Si Thammarat Province, Southern Thailand.

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.

Evaluation of compatibility between formation and Injection water into the Reservoir Rock

Scale deposition represents a critical challenge encountered in water injection systems. The objective of this study is to assess the compatibility between the candidate smart water and the formation water in an oil reservoir rock sample. To achieve this, water samples were collected and analyzed to evaluate the extent of deposit formation under varying temperature and pressure conditions. Additionally, the study also examined the combined influence of temperature and pressure on deposit formation. The results indicated that as the temperature increased, there was a decrease in the concentration of sulfate in the seawater. Taking into account the low concentration of barium (zero) in seawater and the unchanged strontium concentration, the decrease in sulfate concentration can be attributed to the deposition of calcium sulfate. Contrarily, in various ratios of smart water, the sulfate concentration has shown an increase with rising temperatures. This can be attributed to the transformation of sulfite present in the formation water into sulfate due to the influence of temperature. The presence of a low concentration of barium in the formation water resulted in insignificant changes in sulfate concentration due to the precipitation of barium sulfate. Additionally, despite the high concentration of strontium, strontium sulfate deposition did not occur, possibly due to the solubility product constant. The analysis of different smart water ratios under various pressures at ambient temperature revealed that pressure had a minimal effect on the solubility of sulfate and calcium salts. During the sediment analysis, no evidence of strontium sulfate sediment formation was observed. However, the analysis indicated a high possibility of iron sulfide and iron oxide deposits forming. Ultimately, under the experimental conditions, significant sediment formation was not observed.

IoT based Smart Watering System with Remote Monitoring and Control

This paper presents the design and implementation of an IoT-based smart watering system equipped with monitoring and control capabilities using Blynk IoT software. The system integrates various components including soil moisture sensor, water flow sensor, 12V water pump motor, relay module, and NodeMCU ESP8266 microcontroller. Through this setup, users can remotely monitor soil moisture levels, control water flow to plants, and automate watering schedules via the Blynk mobile application. This paper discusses the system architecture, hardware setup, software implementation, and presents experimental results demonstrating the effectiveness and reliability of the proposed solution. Key Words: IoT, Smart watering system, remote monitoring.

Development and Application of Technical Key Performance Indicators (KPIs) for Smart Water Cities (SWCs) Global Standards and Certification Schemes

Smart water cities (SWCs) use advanced technologies for efficient management and preservation of the urban water cycle, strengthening sustainability and improving the quality of life of the residents. This research aims to develop measurement and evaluation tools for SWC key performance indicators (KPIs), focusing on innovative water technologies in establishing unified global standards and certification schemes. The KPIs are categorized based on the stage at which water is being measured, namely the urban water cycle, water disaster management and water supply and treatment. The objective is to assess cities’ use of technologies in providing sufficient water supply, monitoring water quality, strengthening disaster resilience and maintaining and preserving the urban water ecosystem. The assessment is composed of a variety of procedures performed in a quantitative and qualitative manner, the details of which are presented in this study. The developed SWC KPI measurements are used to evaluate the urban water management practices for Busan Eco Delta City, located in Busan, South Korea. Evaluation processes were presented and established, serving as the guideline basis for certification in analyzing future cities, providing integrated and comprehensive information on the status of their urban water system, gathering new techniques, and proposing solutions for smarter measures.

Experimental investigation of the sequence injection effect of sea water and smart water into an offshore carbonate reservoir for enhanced oil recovery

This study explores enhanced oil recovery (EOR) strategies, with a focus on carbonate reservoirs constituting over 60% of global oil discoveries. While “smart water” injection proves effective in EOR for carbonate reservoirs, offshore application challenges arise due to impractical volumes for injection. To address this, we propose a novel continuous injection approach, systematically investigating it on a laboratory scale using the Iranian offshore reservoir, Sivand. Thirty-six contact angle tests and twelve flooding experiments are meticulously conducted, with key ions, potassium, and sulfate, playing pivotal roles. Optimal wettability alteration is observed at 4 times potassium ion concentration in 0–2 times sulfate concentrations, driven by ionic strength and charge interactions. Conversely, at 3–5 times sulfate concentrations, the optimal contact angle shifts to 2 times potassium ion concentration, suggesting a mechanism change linked to increasing sulfate ion ionicity. A significant wettability alteration, evidenced by a 132.8° decrease, occurs in seawater with a twofold concentration of potassium ions and a fivefold concentration of sulfate ions. Micromodel experiments introduce an innovative alternation of smart water and seawater injections. The first scenario, smart water followed by seawater injection, reveals negligible post-seawater injection oil recovery changes. In contrast, the second scenario yields a maximum recovery of 7.9%. The first scenario, however, boasts superior overall sweep efficacy, reaching approximately 43%. This research expands understanding of smart water and seawater injection in EOR, presenting a viable solution for optimizing offshore carbonate reservoir recovery. The insights contribute to evolving EOR methodologies, emphasizing tailored strategies for varying reservoir conditions.

Activity and resolution aware privacy protection for smart water meter databases

Identifying water end-uses from household meter readings yields valuable commercial and environmental insights for both customers and water providers. However, smart water meter databases may expose sensitive information about the activities of metered households, thus raising privacy concerns that limit the possibility of making smart meter data available for research and planning. This paper considers the case where a water provider wishes to publish a database of household water meter traces to be used by water analysts, who are often external entities. This scenario presents privacy risks should an adversary gain access to this database, with the threat of uniquely identifying a household of interest and exposing its water use activities. To mitigate such risks, this paper proposes a process for activity-aware privacy protection and evaluates its effectiveness using real-world and synthetic databases. Our experimental analysis shows that water meter privacy protection is strongly dependent on the type of activity, temporal resolution, and population size. For example, in the case of a high-resolution database, we found that a large base population, small published sample, and 30-second resolution provided optimal trade-offs between privacy and information value. For a low-resolution database with a population of over 3500 households, 1-hour resolution provided strong information value and customisable privacy guarantees depending on the published sample size.

OPTIMIZING PLANT HEALTH THROUGH IoT

Design and implement a smart plant watering system that uses IoT technology to monitor and regulate moisture levels in flower pots. The system should be able to automatically adjust watering frequency and amount based on real-time moisture measurements to ensure plant health and minimize water loss. In addition to our commitment to holistic plant care, an additional feature, NPK fertilizer, has been seamlessly integrated into the system. Containing essential nutrients nitrogen, potassium and phosphorus, this feature optimizes plant growth and health. This system automatically applies fertilizer according to a pre-set schedule or on-the-fly adjustments, creating the perfect solution for efficient and sustainable plant growth. The main goal is to create convenient and sustainable solutions. Meets the needs of both experienced and novice plant enthusiasts. Keywords: Internet of Things, NPK fertilizer, humidity sensor, WiFi module, buzzer, motor.