Smart Water Research Articles

Smart watering of ornamental plants: exploring the potential of decision trees in precision agriculture based on IoT

Ornamental plant farmers face various challenges due to climate change and environmental stress that significantly affect plant health and growth. This research overcomes these challenges by developing an intelligent watering system that uses internet of things (IoT) technology and decision trees (DTs) algorithms to optimize the use of planting land by ensuring plants grow in the most optimal conditions, both in terms of water and nutrients and increase land productivity. The system is built by integrating various sensors to monitor soil moisture, air humidity, temperature, and light intensity in real-time. The collected data is used to automate watering schedules and provide recommendations on suitable plant species based on the soil nutrient content of nitrogen (N), phosphorus (P), and potassium (K). The use of the DTs algorithm helps in analyzing the data from the sensors and providing recommendations on the most suitable plants for the land. The smart watering system was tested in three zones, each simulating a different watering scenario, and successfully maintained optimal conditions for plant growth in each zone. The machine learning (ML) model with the DTs algorithm can predict the right type of ornamental plants based on the existing land conditions in three watering zones, with an accuracy of 89 %, 90 %, and 91 %, respectively. Furthermore, farmers can follow these recommendations to minimize damage and death of plants so that the level of productivity on the land becomes optimal.

Feasibility study for lifestyle monitoring of older adults living independently at home using smart water meter data

Feasibility study for lifestyle monitoring of older adults living independently at home using smart water meter data

Water neutrality: Concept, challenges, policies, and recommendations

Agriculture, urbanisation, and industrial growth have significantly increased water demand, leading to water pollution, necessitating the protection of rivers, wetlands, and ecosystems. Hence, a strategy is needed that is aligned with Sustainable Development Goal (SDG) 6, which includes ensuring safe and affordable drinking water (SDG 6.1), implementing integrated water resources management (SDG 6.5), and protecting and restoring water-related ecosystems (SDG 6.6). This review article introduces the concept of water neutrality (WN), a planning approach for new urban developments aimed at minimising impacts on water security. The peer-reviewed articles and governmental reports published between 2014 and 2024 provide an overview of the current water situation. It also explores the potential benefits of WN, evaluates existing policies and case studies, and offers actionable recommendations. The study reveals that implementing water-related policies often faces challenges, such as political commitment, insufficient funding, and weak monitoring systems, particularly in developing countries. In India, water policies primarily focus on irrigation, drinking water supply, and pollution control. However, these policies struggle due to weak enforcement and conflicting stakeholder interests. The study recommends significant policy reforms and the adoption of new approaches to achieve WN, which promote integrated water resource management, strengthen governance, pricing mechanisms, advanced pollution control technologies, climate-resilient infrastructure, and public awareness, all while aligning with SDG 6. Innovative solutions, such as smart irrigation systems and water recycling programs, offer promising paths to address water scarcity. Additionally, international collaborations can further support these goals. Integrating WN into SDG 6 has the potential to significantly enhance global water security and sustainability.

Assessment of sensor-based automatic smart watering unit for paddy nurseries under Indian perspective

A sensor-based automatic watering unit was developed to irrigate the paddy seedling trays. The watering unit comprised six flat spray nozzles with a direct current (DC) water pump and flow sensor, and these were kept at the top of the conveyor frame, which was actuated by a DC water pump with a solenoid valve to drop the water in trays. The actuation of the DC solenoid valve was controlled by a limit switch fitted to the conveyor frame, which sensed the moving trays on the conveyor. The performance of the developed unit was evaluated, and process optimization was performed using the Inscribed Central Composite Design in Response Surface Methodology. The effect of main operating parameters- chain conveyor speed (0.123, 0.196, and 0.27 m/s), spray operating pressure (1.5, 3.0, and 4.5 kg/cm2), and spray height (10,30 and 50 cm) on the performance of the sensor-based automatic watering unit was studied in terms of the amount of water spray requirement and coefficient of water spray uniformity in trays. The responses of the unit at its optimal parameter operation (spray operating pressure 4.5 kg/cm2, spray height 10 cm, and chain conveyor speed 0.14 m/s) were found to be 1.31 L/tray as the amount of water spray requirement and 92.41 % as the coefficient of water spray uniformity of the unit. Hence, it can be an efficient substitute with significantly less power consumption 41 W with a higher working capacity of 780 trays/h for watering into the paddy tray nursery. The developed sensor-based automatic smart watering unit saves irrigation water per square area of 68.90 %, 76.13 %, and 82.31 % compared to existing drip, sprinkler, and flood irrigation methods in mat-type paddy nurseries. The developed sensor-based automatic smart watering unit saves the watering cost INR 11,567.16 (140.02 USD) per hectare, reducing cost by 63.7 % compared to manual watering in the mat-type nursery preparation. Thus, an automatic smart watering unit in a paddy tray nursery offers consistent and uniform watering from an Indian perspective, promoting uniform germination, crop growth, and establishment. It conserves water by ensuring the right amount of water is applied at the right time, avoiding overwatering or under-watering.

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.

Smart and cost-effective water quality monitoring system: University of Baghdad canal case study

Polluted water causes diseases, affecting the ecosystem. Water quality is crucial due to high demand. Regular monitoring is essential for a consistent supply of fresh water. Internet of Things (IoT) advancements enable real-time monitoring and timely intervention for water quality issues. The current investigation evaluated a proposed smart, cost effective, high-efficacy IoT application of a water quality detection device that real-time monitors six measurement points along the University of Baghdad canal for five water quality parameters. With the Arduino platform and specific sensor properties, a low-cost monitoring system has been built. The data is shown on an LCD monitoring screen and presented on a Wi-Fi module before being transferred to the cloud and presented on a smartphone and internet platform. The results from the monitoring system for both winter and spring water quality are within the permissible limit according to standard limitations were average pH 7.687, total dissolved solid 406.75 mg/l, turbidity 25.883 NTU, and electrical conductivity 630.8 µS/cm. This framework may successfully offer an environment where there is enough water for agricultural purposes while also keeping a careful check on the contamination of water resources. On the other hand, this permits a well-managed water quality standard.

Alleviating urban pluvial floods via dual-use water plazas orchestrated by predictive algorithms

Old urban areas, characterized by complex land use and inadequate infrastructure, exhibit high vulnerability to precipitation extremes. The integration of dual-use public infrastructure stands at the forefront of planning initiatives to enhance urban flood resilience. This study introduces a pioneering Smart Water Plaza (SWP) system, orchestrated by predictive algorithms, to mitigate pluvial floods. A high-risk old neighborhood (4.5 km2) in Changsha, China, is selected as a case for empirical analysis. Two types of SWPs—in-situ detention, and runoff-receiving units—are initially established based on spatial mapping and potential analysis. The core feature of the SWP system is a multi-horizon 2D-1D model predictive control (MPC), which directly verifies the suitability of terrain configurations and evaluates temporal efficiency. The study plots the performance curves of the SWPs under various design rainfalls and demonstrates flood dynamics using an actual torrential event. The findings indicate that SWPs’ resilience contribution index for the stormwater system ranges from 9 % to 38 %. Furthermore, SWPs covering only 5 % of the site lead to an obvious decrease in flooded areas, with reductions ranging from 46 % to 48 %. These outcomes highlight the disaster mitigation potential inherent in multifunctional public spaces for Sponge City transformation and urban renewal efforts.

Digital Twin Smart Water Conservancy: Status, Challenges, and Prospects

Digital twin technology, a new type of digital technology emerging in recent years, realizes real-time simulation, prediction and optimization by digitally modeling the physical world, providing a new idea and method for the design, operation and management of water conservancy projects, which is of great significance for the realization of the transformation of water conservancy informatization to intelligent water conservancy. In view of this, this paper systematically discusses the concept and development history of digital twin smart water conservancy, compares its differences with traditional water conservancy models, and further proposes the digital twin smart water conservancy five-dimensional model. Based on the five-dimensional model of digital twin water conservancy, the research progress of digital twin smart water conservancy is summarized by focusing on six aspects, namely digital twin water conservancy data perception, data transmission, data analysis and processing, digital twin water conservancy model construction, digital twin water conservancy interaction and collaboration and digital twin water conservancy service application, and the challenges and problems of digital twin technology in the application of smart water conservancy. Finally, the development trend of digital twin technology and the direction of technological breakthroughs are envisioned, aiming to provide reference and guidance for the research on digital twin technology in the field of smart water conservancy and to promote the further development of the field.

Experimental investigation of carbamide-assisted smart water flooding for enhancing heavy oil recovery from carbonate reservoirs

This study explores the impact of carbamide at different concentrations on smart water flooding into carbonate reservoirs. The aim is to find out how the presence of carbamide can influence the mechanisms of smart water flooding at rock/brine and oil/brine interfaces. The results revealed that interfacial tension (IFT) reduces as the carbamide concentration increases in the brine. Indeed, the carbamide decomposes at elevated temperatures, and in-situ surface active agents are formed at the oil/brine interface, reducing IFT values. Further, while the dilution of seawater (SW) is unable to change the oil-wet wettability, the carbamide can make the carbonate surface more water-wet by elevating its concentration. Additionally, the corresponding mechanisms of this wettability alteration would be the formation of in-situ surface active agents, electrostatic interactions, and hydrogen bond formation at the rock/brine interface. Among K+, Mg2+, and SO42−, adjusting the concentration of sulfate ions in SW leads to greater IFT reduction in the presence of carbamide. In addition, the contact angle is further reduced as K+ concentration is doubled in the solution; however, this reduction is not significant as compared to Mg2+ and SO42−. Also, ten-times diluted seawater (10dsw) produced more micro-dispersions in the oil phase. Nevertheless, the presence of carbamide in 10dws had a negative impact on the micro-dispersion formation, which is one of the driving mechanisms of smart water flooding. At a lower carbamide concentration (1000 ppm), more carboxylic acids and ketones were observed in the brines due to the partitioning and dissociation of naphthenic acids. On the contrary, as carbamide concentration increased to 10000 ppm, amounts of carboxylic acids declined due to the production of in-situ surface active agents at the oil/brine interface. Bottle test experiments indicated that water-in-oil emulsions were generated in the presence of carbamide. The mean water droplet size grew as carbamide concentration rose to 25000 ppm; nonetheless, further increase in the carbamide concentration resulted in mean droplet size reduction. Calcite-coated micromodel flooding illustrated that oil recovery of 21.6, 24, and 27.2 % was found at carbamide concentrations of 1000, 10000, and 100000 ppm. Furthermore, a three-time increase in SO42− concentration at 10000 ppm of carbamide offered a better performance in boosting oil recovery than tuning K+ and Mg2+ concentrations in carbamide/SW solutions. The findings of this study underscore the promising prospects of leveraging carbamide to enhance the efficacy of smart water solutions in carbonate reservoirs, particularly regarding IFT modification, wettability alteration, and oil recovery enhancement.

Detecting Cracks in Intelligent Carbon-Based TRC Elements Through Wetting Events

Textile reinforced concrete (TRC) technology enables to construct sustainable and environmentally friendly thin-walled structural elements with self-sensory capabilities. The self-sensory concept is based on using high-strength and electrically conductive yarns that are simultaneously used as the main reinforcement system and as the sensory agent. By connecting the two ends of the yarns to either direct current (DC) or alternating current (AC) electrical circuits, it is possible to measure changes in the electrical readings and to further correlate them to the structural health. Studies in the literature validated the concept by using carbon yarns as hybrid monitoring agents. Most of them provided integrated assessments of global structural health but lacked the capability to pinpoint individual cracks. The current study offers a new identification procedure to locate cracks and to estimate their severity by combining two advanced monitoring procedures, that is by adopting the Time Domain Reflectometer (TDR) analysis; and by conceptually using the smart water leakage detection method. Both methods take advantage that a pair of carbon yarns can be positioned parallel to each other and connected to the DAQ system. In case of TDR method, one yarn functions as the signal transmitter and the other as insulation. In case of water leakage detection method, the electrical linkage between the two yarns changes the electrical characterization of the electrical circuit and enables the monitoring capabilities. The proposed study argues that a new identification procedure that benefits from each method can be developed and yields to smart identification procedure that has the capability to locate the crack and estimate its severity. The study will demonstrate the feasibility of the new procedure by experimental study. Results from this study will take a significant step toward developing reliable smart carbon-based TRC structures.

Solar Water Heater Using an IoT

Abstract: Traditional water heaters use a lot of electricity to heat water. Traditional water heaters require a heating coil to heat water, which uses at least 2000 watts. It also requires human intervention. For this reason, we utilize solar energy. Solar energy is not only a clean and renewable alternative to fossil fuels, but it is also environmentally friendly, has a wide range of applications, and has low maintenance costs. Therefore, the aim of this project is to convert solar energy into electrical energy and utilize solar energy to heat water. The solar hot water system includes IoT, solar panels, various modules and various sensors. Based on this measurement, we provide a smart hot water system through a combination of Internet of Things technologies and business processes and decision support systems. Maintaining a traditional water system is inconvenient but often effective when regular human intervention is required. In this project work, the complete workflow of the system is presented.

Review on Different Methods for Smart Dam Operation and Water Monitoring

Abstract: This complete literature review digs into the complex world of automated dam operations and water quality management, emphasising key advances and developing techniques in this crucial subject. The study focuses on the integration of sensor technologies, IoT systems, and data analytics to explain how these advances have transformed dam management and water quality preservation. The study gives essential insights into the cutting-edge approaches used in automated dam operations and water quality management by thoroughly examining numerous studies and research findings. Discussions on the use of real-time monitoring systems to track critical water parameters, as well as the use of predictive modelling tools to detect possible problems with water quality early on, are especially relevant. This literature review not only emphasises the importance of technological advancements in improving operational efficiency and environmental stewardship, but it also lays the groundwork for future exploration and refinement of thee methodologies in the pursuit of sustainable water resource management.

Smart Water–Polymer Hybrid Flooding in Heavy Oil Sandstone Reservoirs: A Detailed Review and Guidelines for Future Applications

Smart Water–Polymer Hybrid Flooding in Heavy Oil Sandstone Reservoirs: A Detailed Review and Guidelines for Future Applications

Towards Reliability in Smart Water Sensing Technology: Evaluating Classical Machine Learning Models for Outlier Detection.

In recent years, smart water sensing technology has played a crucial role in water management, addressing the pressing need for efficient monitoring and control of water resources analysis. The challenge in smart water sensing technology resides in ensuring the reliability and accuracy of the data collected by sensors. Outliers are a well-known problem in smart sensing as they can negatively affect the viability of useful analysis and make it difficult to evaluate pertinent data. In this study, we evaluate the performance of four sensors: electrical conductivity (EC), dissolved oxygen (DO), temperature (Temp), and pH. We implement four classical machine learning models: support vector machine (SVM), artifical neural network (ANN), decision tree (DT), and isolated forest (iForest)-based outlier detection as a pre-processing step before visualizing the data. The dataset was collected by a real-time smart water sensing monitoring system installed in Brussels' lakes, rivers, and ponds. The obtained results clearly show that the SVM outperforms the other models, showing 98.38% F1-score rates for pH, 96.98% F1-score rates for temp, 97.88% F1-score rates for DO, and 98.11% F1-score rates for EC. Furthermore, ANN also achieves a significant results, establishing it as a viable alternative.

Evaluating the Smart Technology Level of Water Purification Plants

Key TakeawaysThe features of a smart water purification plant (SWPP) can include autonomous operation of treatment processes, energy management optimization, and facility preservation, using approaches that include artificial intelligence technology.Evaluation indicators were developed to measure the level of technology in K‐water's SWPPs.Practical demonstration at three K‐water SWPPs was used to validate the effectiveness of these indicators.

Decentralized periodic event‐triggered control for smart water distribution systems via digital twin approach

AbstractDecentralized periodic event‐triggered control can reduce the working time and data transmissions in the sensor nodes for feedback control loops, making it suitable for wireless networked control systems, especially those with large physical scales, such as smart water distribution systems. However, the design of this control approach requires accurate system model, which is normally difficult to obtain. In this work, we assume a digital twin of the original system can be obtained by some modeling and simulation tools, for example, Simscape in MATLAB/Simulink for water distribution systems, and thus present an event‐triggered controller design approach. We first present the design of such an event‐triggered controller for the digital twin; and then analyze the condition under which if both the original system and its digital twin can be satisfied, the same controller can work for the original system and some type of stability can be guaranteed at the same time. The proposed approach is applied in a smart water distribution system to show the feasibility.

Development of IoT based intelligent irrigation system using particle swarm optimization and XGBoost techniques

A crop needs regular watering throughout its life to grow well. Irrigation improves food growth. Machines irrigate plants. The dry Sahel, which gets a lot of rain during the summer season but is dry in winter, needs irrigation. When it doesn't rain enough, crops need watering. By constantly monitoring soil moisture, humidity, temperature, and pH, precision agriculture reduces water use and increases crop output. Precision gardening uses less water. In many wealthy nations, efficient farming requires the internet of things (IoT). Particle swarm optimization (PSO) and XGBoost are used in this IoT-based intelligent watering system. Humidity and moisture sensors gather soil data at grass roots. Sensors constantly gather this data. These data are useless for smart watering. PSOselects smart watering data. This reduces central cloud info storage. Then, machine learning methods are trained using soil humidity, moisture, crop, and weather data. These programs can calculate a crop's water requirements. IoT devices control irrigation system water flow and results in saving fresh water. XGBoost algorithm is saving water from 23% to 27% for different crops.

Comparative Analysis of Supervised Classification Algorithms for Residential Water End Uses

AbstractWater sustainability in the built environment requires an accurate estimation of residential water end uses (e.g., showers, toilets, faucets, etc.). In this study, we evaluate the performance of four models (Random Forest, RF; Support Vector Machines, SVM; Logistic Regression, Log‐reg; and Neural Networks, NN) for residential water end‐use classification using actual (measured) and synthetic labeled data sets. We generated synthetic labeled data using Conditional Tabular Generative Adversarial Networks. We then utilized grid search to train each model on their respective optimized hyperparameters. The RF model exhibited the best model performance overall, while the Log‐reg model had the shortest execution times under different balanced and imbalanced (based on number of events per class) synthetic data scenarios, demonstrating a computationally efficient alternative for RF for specific end uses. The NN model exhibited high performance with the tradeoff of longer execution times compared to the other classification models. In the balanced data set scenario, all models achieved closely aligned F1‐scores, ranging from 0.83 to 0.90. However, when faced with imbalanced data reflective of actual conditions, both the SVM and Log‐reg models showed inferior performance compared to the RF and NN models. Overall, we concluded that decision tree‐based models emerge as the optimal choice for classification tasks in the context of water end‐use data. Our study advances residential smart water metering systems through creating synthetic labeled end‐use data and providing insight into the strengths and weaknesses of various supervised machine learning classifiers for end‐use identification.

Nonlinear Augmented Reality Enabled Smart Water Leakage Identification and Infrastructure Management of Pipeline System

Nonlinear Augmented Reality Enabled Smart Water Leakage Identification and Infrastructure Management of Pipeline System