Search Results (283)

The objective of this study is to define groundwater management zones for a complex deformed and fissured Precambrian karst aquifer, which underlies one of the most important agricultural areas in the semi-arid region of Irecê, Bahia, Brazil. It is an unconfined aquifer, hundreds of meters thick, resulting from a large sequence of carbonates piled up by thrust faults during tectonic plate collisions. Groundwater recharge and flow in this aquifer are greatly influenced by karst features, through the high density of sinkholes and vertical wells. Over the past four decades, population and agricultural activities have increased in the region, resulting in unsustainable groundwater withdrawal and, at the same time, water quality degradation. Therefore, it is important to develop legal and environmental management strategies. This work proposes the division of the karst area into three well-defined management zones by mapping karst structures, land use, and urban occupation, as well as the concentrations of chloride and nitrate in the region’s groundwater. Zone 1 in the north possesses the lowest levels of karstification, anthropization, and contamination, while zone 2 in the central region has the highest levels and zone 3 in the south ranging in-between (except for stronger karstification). The delimitation of management zones will contribute to the development and implementation of optimized zone-specific groundwater preservation and restoration strategies. Full article

Cyber-physical systems such as Supervisory Control and Data Acquisition (SCADA) have been applied in industrial automation and infrastructure management for decades. They are hybrid tools for administration, monitoring, and continuous control of real physical systems through their computational representation. SCADA systems have evolved along with computing technology, from their beginnings with low-performance computers, monochrome monitors and communication networks with a range of a few hundred meters, to high-performance systems with advanced 3D graphics and wired and wireless computer networks. This article presents a methodology for the design of a SCADA system with a 3D Visualization for Drinking Water Supply, and its implementation in the Lerma Basin System of Mexico City as a case study. The monitoring of water consumption from the wells is presented, as well as the pressure levels throughout the system. The 3D visualization is generated from the GIS information and the communication is carried out using a hybrid radio frequency transmission system, satellite, and telephone network. The pumps that extract water from each well are teleoperated and monitored in real time. The developed system can be scaled to generate a simulator of water behavior of the Lerma Basin System and perform contingency planning. Full article

The water–energy nexus (WEN) is of great significance due to the strong interdependence between the energy and water sectors. Nevertheless, water leakage in water distribution networks (WDNs), which is often ignored in existing WEN operation models, causes notable water and energy losses. In this research, a cooperative operation model for WEN considering WDN water leakage is put forward. A hybrid stochastic–information gap decision theory (IGDT) method was tailored in this study to properly manage the probabilistic uncertainties associated with renewable generation, electrical and water demand in the WEN, and water leakage with limited information to enhance the robustness of the operation strategies of the WEN under complex operational conditions. The proposed model and method were validated on a modified IEEE 33-bus system integrated with a 15-node commercial WDN. The co-optimization model reduced the operational cost by 23.01% compared to the independent operation model. When considering water leakage, the joint optimization resolved the water supply shortage issue caused by ignoring leakage and reduced the water purchase volume by 94.44 cubic meters through coordinated optimization. These quantitative results strongly demonstrate the effectiveness of the proposed framework. Full article

Water, the lifeblood of our planet, sustains ecosystems, economies, and communities. However, climate change and increasing hydrological variability have exacerbated global water scarcity, threatening livelihoods and economic stability. According to the United Nations, over 2 billion people currently live in water-stressed regions, a figure expected to rise significantly by 2030. To address this urgent challenge, this study proposes an AI-driven anomaly detection framework for smart water metering networks (SWMNs) using machine learning (ML) techniques and data resampling methods to enhance water conservation efforts. This research utilizes 6 years of monthly water consumption data from 1375 households from Location A, Windhoek, Namibia, and applies support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbors (kNN) models within ensemble learning strategies. A significant challenge in real-world datasets is class imbalance, which can reduce model reliability in detecting abnormal patterns. To address this, we employed data resampling techniques including random undersampling (RUS), SMOTE, and SMOTEENN. Among these, SMOTEENN achieved the best overall performance for individual models, with the RF classifier reaching an accuracy of 99.5% and an AUC score of 0.998. Ensemble learning approaches also yielded strong results, with the stacking ensemble achieving 99.6% accuracy, followed by soft voting at 99.2% and hard voting at 98.1%. These results highlight the effectiveness of ensemble methods and advanced sampling techniques in improving anomaly detection under class-imbalanced conditions. To the best of our knowledge, this is the first study to explore and evaluate the combined use of ensemble learning and resampling techniques for ML-based anomaly detection in SWMNs. By integrating artificial intelligence into water systems, this work lays the foundation for scalable, secure, and efficient smart water management solutions, contributing to global efforts in sustainable water governance. Full article

Understanding the characteristics of residential hot-water consumption can be useful for developing effective water-conservation strategies in response to increasing pressure on natural resources. This study systematically investigates residential hot-water consumption through direct monitoring of over 40 domestic fixtures (belonging to six different end-use categories) in five Italian households, recorded over a period ranging from approximately two weeks to nearly four months, and using smart meters with 5 min resolution. A multi-step analysis is applied—at both household and end-use levels, explicitly differentiating tap uses by purpose and location—to (i) quantify daily per capita hot-water consumption, (ii) calculate hot-water ratios, and (iii) assess daily profiles. The results show an average total water consumption of 106.7 L/person/day, with at least 26.1% attributed to hot water. In addition, daily profiles reveal distinct patterns across end uses: hot- and cold-water consumption at kitchen sinks are not aligned over time (with cold water peaking before meals and hot water used predominantly afterward), while bathroom taps show more synchronized use and a marked evening peak in hot-water consumption. Study findings—along with the related open-access dataset—provide a valuable benchmark based on field measurements to support in the process of water demand modeling and the development of targeted demand-management strategies. Full article

Soil moisture dynamics are a key parameter in regulating agricultural productivity and ecosystem functioning. The accurate monitoring and quantitative retrieval of soil moisture play a crucial role in optimizing agricultural water resource management. In recent years, the development of multi-source remote sensing technologies—such as high spatiotemporal resolution optical, radar, and thermal infrared sensors—has opened new avenues for efficient soil moisture retrieval. However, the accuracy of soil moisture retrieval decreases significantly when the soil is covered by vegetation. This study proposes a multi-modal remote sensing collaborative retrieval framework that integrates UAV-based multispectral imagery, Sentinel-1 radar data, and in situ ground sampling. By incorporating a vegetation suppression technique, a random-forest-based quantitative soil moisture model was constructed to specifically address the interference caused by dense vegetation during crop growing seasons. The results demonstrate that the retrieval performance of the model was significantly improved across different soil depths (0–5 cm, 5–10 cm, 10–15 cm, 15–20 cm). After vegetation suppression, the coefficient of determination (R²) exceeded 0.8 for all soil layers, while the mean absolute error (MAE) decreased by 35.1% to 49.8%. This research innovatively integrates optical–radar–thermal multi-source data and a physically driven vegetation suppression strategy to achieve high-accuracy, meter-scale dynamic mapping of soil moisture in vegetated areas. The proposed method provides a reliable technical foundation for precision irrigation and drought early warning. Full article

Accurate localization of buried water pipelines in rural areas is crucial for maintenance and leak management but is often hindered by outdated maps and the limitations of traditional geophysical methods. This study aimed to develop and validate a multi-source remote-sensing workflow, integrating UAV (unmanned aerial vehicle)-borne near-infrared (NIR) surveys, multi-temporal Sentinel-2 imagery, and historical Google Earth orthophotos to precisely map pipeline locations and establish a surface baseline for future monitoring. Each dataset was processed within a unified least-squares framework to delineate pipeline axes from surface anomalies (vegetation stress, soil discoloration, and proxies) and rigorously quantify positional uncertainty, with findings validated against RTK-GNSS (Real-Time Kinematic—Global Navigation Satellite System) surveys of an excavated trench. The combined approach yielded sub-meter accuracy (±0.3 m) with UAV data, meter-scale precision (≈±1 m) with Google Earth, and precision up to several meters (±13.0 m) with Sentinel-2, significantly improving upon inaccurate legacy maps (up to a 300 m divergence) and successfully guiding excavation to locate a pipeline segment. The methodology demonstrated seasonal variability in detection capabilities, with optimal UAV-based identification occurring during early-vegetation growth phases (NDVI, Normalized Difference Vegetation Index ≈ 0.30–0.45) and post-harvest periods. A Sentinel-2 analysis of 221 cloud-free scenes revealed persistent soil discoloration patterns spanning 15–30 m in width, while Google Earth historical imagery provided crucial bridging data with intermediate spatial and temporal resolution. Ground-truth validation confirmed the pipeline location within 0.4 m of the Google Earth-derived position. This integrated, cost-effective workflow provides a transferable methodology for enhanced pipeline mapping and establishes a vital baseline of surface signatures, enabling more effective future monitoring and proactive maintenance to detect leaks or structural failures. This methodology is particularly valuable for water utility companies, municipal infrastructure managers, consulting engineers specializing in buried utilities, and remote-sensing practitioners working in pipeline detection and monitoring applications. Full article

Water, an essential asset for life and growth, is under growing pressure due to climate change, overpopulation, pollution, and industrialization. At the same time, water distribution within cities relies on piping networks that are over 30 years old and thereby prone to leaks, cracking, and losses. Taking this into account, non-revenue water (i.e., water that is distributed to homes and facilities but not returning revenues) is estimated at almost 50%. To this end, intelligent water management via computational advanced tools is required in order to optimize water usage, to mitigate losses, and, more importantly, to ensure sustainability. To address this issue, a case study was developed in this paper, following a step-by-step methodology for the city of Heraklion, Greece, in order to introduce an intelligent water management system that integrates advanced technologies into the aging water distribution infrastructure. The first step involved the digitalization of the network’s spatial data using geographic information systems (GIS), aiming at enhancing the accuracy and accessibility of water asset mapping. This methodology allowed for the creation of a framework that formed a “digital twin”, facilitating real-time analysis and effective water management. Digital twins were developed upon real-time data, validated models, or a combination of the above in order to accurately capture, simulate, and predict the operation of the real system/process, such as water distribution networks. The next step involved the incorporation of a hydraulic simulation and modeling tool that was able to analyze and calculate accurate water flow parameters (e.g., velocity, flowrate), pressure distributions, and potential inefficiencies within the network (e.g., loss of mass balance in/out of the district metered areas). This combination provided a comprehensive overview of the water system’s functionality, fostering decision-making and operational adjustments. Lastly, automatic meter reading (AMR) devices could then provide real-time data on water consumption and pressure throughout the network. These smart water meters enabled continuous monitoring and recording of anomaly detections and allowed for enhanced control over water distribution. All of the above were implemented and depicted in a web-based environment that allows users to detect water meters, check water consumption within specific time-periods, and perform real-time simulations of the implemented water network. Full article

While digital transformation in e-commerce receives the most publicity, applications in energy and water utilities have been ongoing for decades. Using a methodology based on a systematic review, the paper offers a model of how it occurs in water utilities, reviews experiences from the field, and derives lessons learned to create a road map for future research and implementation. Innovation in water utilities occurs more in the field than through organized research, and utilities share their experiences globally through networks such as water associations, focus groups, and media outlets. Their digital transformation journeys are evident in business practices, operations, and asset management, including methods like decision support systems, SCADA systems, digital twins, and process optimization. Meanwhile, they operate traditional regulated services while being challenged by issues like aging infrastructure and workforce capacity. They operate complex and expensive distribution systems that require grafting of new controls onto older systems with vulnerable components. Digital transformation in utilities is driven by return on investment and regulatory and workforce constraints and leads to cautious adoption of innovative methods unless required by external pressures. Utility adoption occurs gradually as digital tools help utilities to leverage system data for maintenance management, system renewal, and water loss control. Digital twins offer the advantages of enterprise data, decision support, and simulation models and can support distribution system optimization by integrating advanced metering infrastructure devices and water loss control through more granular pressure control. Models to anticipate water main breaks can also be included. With such advances, concerns about cyber security will grow. The lessons learned from the review indicate that research and development for new digital tools will continue, but utility adoption will continue to evolve slowly, even as many utilities globally are too stressed with difficult issues to adopt them. Rather than rely on government and academics for research support, utilities will need help from their support community of regulators, consultants, vendors, and all researchers to navigate the pathways that lie ahead. Full article

Cultivating crops in the hot, arid conditions of the Arabian Peninsula often requires irrigation with brackish water, which exposes plants to salinity and heat stress. Timely, cost-effective monitoring of plant health can significantly enhance crop management. In this context, remote sensing techniques offer promising alternatives. This study evaluates several low-cost, ground-level remote sensing methods and compares them with benchmark analytical techniques for assessing salt stress in two economically important woody species, moringa and pomegranate. The species were irrigated under three salinity levels: low (2 dS m⁻¹), medium (5 dS m⁻¹), and high (10 dS m⁻¹). Remote sensing tools included RGB, multispectral, and thermal cameras mounted on selfie sticks for canopy imaging, as well as portable leaf pigment and chlorophyll fluorescence meters. Analytical benchmarks included sodium (Na) accumulation, carbon isotope composition (δ¹³C), and nitrogen (N) concentration in leaf dry matter. As salinity increased from low to medium, canopy temperatures, vegetation indices, and δ¹³C values rose. However, increasing salinity from medium to high levels led to a rise in Na accumulation without further significant changes in other remote sensing and analytical parameters. In moringa and across the three salinity levels, the Normalized Difference Red Edge (NDRE) and leaf chlorophyll content on an area basis showed significant correlations with δ¹³C (r = 0.758, p < 0.001; r = 0.423, p < 0.05) and N (r = 0.482, p < 0.01; r = 0.520, p < 0.01). In pomegranate, the Normalized Difference Vegetation Index (NDVI) and chlorophyll were strongly correlated with δ¹³C (r = 0.633, p < 0.01 and r = 0.767, p < 0.001) and N (r = 0.832, p < 0.001 and r = 0.770, p < 0.001). Remote sensing was particularly effective at detecting plant responses between low and medium salinity, with stronger correlations observed in pomegranate. Full article

Background/Objectives: Head and neck lymphedema (HNL) is a common complication after head and neck cancer (HNC) treatment. Reliable and feasible assessment methods are essential for monitoring and management. This study aimed to evaluate the reliability and clinical feasibility of three methods for assessing external HNL in HNC patients: local tissue water (%) using the MoistureMeterD Compact (MMDC), neck circumference using a tape measure, and dermal thickness using B-mode ultrasound. Methods: Thirty-three HNC patients with potential HNL were included. Measurements were performed on the same day, twice by the same rater and once by a different rater. Intraclass correlation coefficients (ICC^2,1), (relative) standard error of measurement ((%)SEM), smallest real difference (SRD), systematic differences across measurement occasions, and Bland–Altman plots with 95% limits of agreement were analyzed. Time efficiency and clinical limitations were assessed. As an exploratory analysis, Spearman correlations among methods were examined. Results: All methods demonstrated moderate to very strong reliability (ICCs^2,1 0.781–0.994), except dermal thickness (ICCs^2,1 0.136–0.354). Differences between raters and within one rater were not clinically meaningful. Neck circumference showed the highest reliability (ICCs^2,1 0.958–0.994) and was the fastest to perform with the fewest limitations. The methods showed weak correlations with each other. Conclusions: Neck circumference was the most reliable and time-efficient method assessing HNL in clinical practice but is limited to the neck region. For the head, local tissue water assessment seems the most reliable and feasible. The methods assess different aspects of HNL. Further research should confirm how these methods can complement each another. Full article

The accelerated expansion of urban areas has significantly increased the complexity of managing water distribution systems. Network sectorization into smaller, independently controlled areas is often highlighted as an important measure to enhance operational security and reduce water losses in networks. However, identifying the optimal sectorization strategy is challenging due to the vast number of possible combinations, and existing methods still present practical limitations. This study proposes a hybrid model for the optimal design of district-metered areas in water distribution systems. The methodology combines graph theory, the Dijkstra shortest path algorithm (DSP), and the meta-heuristic binary particle swarm optimization (BPSO) algorithm. Structuring the topology of the water distribution network using graphs allows the identification of existing connections between the network components. By DSP, the shortest paths from the reservoir to the consumption points were determined, while the proposed BPSO sought the best combination of pipe conditions (open or closed) while meeting the constraint conditions. The application of the model to three real water distribution systems in João Pessoa, in northeastern Brazil, demonstrated its efficiency in sectorization projects, providing optimal solutions that meet the imposed constraints. The results highlight the model’s potential to optimize costs and enhance decision-making in water utility projects. Full article

Armillaria root rot (ARR) is a fungal disease caused by Desarmillaria caespitosa and the leading cause of peach tree decline in the Southeastern U.S. It affects the roots and lower stems of trees, leading to the decay of the tree’s root system. Planting peach trees shallow on berms and excavating soil around the root collar after two years can extend the economic life of infected trees. However, berms pose operational challenges, including elevation changes, soil erosion from water flow, and herbicide and fertilizer runoff, thereby reducing orchard management efficiency. This study aimed to develop a tractor-mounted rotary tillage method to flatten the area between peach trees planted on berms, improving safety and reducing runoff. A custom paddle wheel attachment (20.3 cm height, 30.5 cm length) was retrofitted to an existing mechanical orchard weed management implement equipped with a hydraulic rotary head. A hydraulic flow meter, two pressure transducers, and an RTK-GPS receiver were integrated with a wireless data acquisition system to monitor the paddle wheel rotational speed and tractor ground speed during field trials. The effects of three paddle wheel speeds (132, 177, and 204 RPM) and three tractor ground speeds (1.65, 2.255, and 3.08 km/h) were evaluated in two orchards with Cecil sandy loam soil (bulk density: 1.93 g/cm³; slope: 2–6%). The paddle wheel speed had a greater influence on the torque and power requirements than the tractor ground speed. The combination of a 177 RPM paddle speed and 3 km/h tractor speed resulted in the smoothest soil surface with minimum torque demand, indicating this setting as optimal for flattening berms in similar soil conditions. Future research will include optimizing the paddle wheel structure and equipping the berm leveling machine with tree detection sensors to control the rotary head position. Full article

Smart cities integrate advanced technologies, data-driven decision-making, and interconnected infrastructure to enhance urban living and resource efficiency. Among these, Smart Water Management (SWM) is crucial for optimizing water distribution and reducing Non-Revenue Water (NRW) losses, a persistent challenge for utilities worldwide. Water leaks contribute significantly to NRW, necessitating real-time leak detection and management systems to minimize detection time and human effort. Achieving this requires seamless integration of SWM technologies, including advanced metering infrastructure, the Internet of Things (IoT), and Artificial Intelligence (AI). While previous studies have explored various leak detection techniques, many lack a focused analysis of real-time data integration and automated alerts in SWM systems. This Systematic Literature Review (SLR) addresses this gap by examining advancements in automatic data collection, leak detection models, and real-time alert mechanisms. The findings highlight the growing potential of data-driven approaches to enhance leak detection accuracy and efficiency, particularly those leveraging flow and pressure data. Despite advancements, model accuracy, scalability, and real-world applicability remain. This review provides critical insights for future research, guiding the development of automated, AI-driven leak management systems to improve water distribution, minimize losses, and enhance sustainability in smart cities. Full article

In response to the increasingly severe energy consumption problem and to promote energy saving and emission reduction, this study aims to design and apply an energy management system platform based on artificial intelligence (AI) technology. The system adopts sensor technology and data acquisition equipment to monitor various types of energy consumption in buildings in real time, efficiently process and predict these data through machine learning algorithms, and finally visualize the results. The system is functionally complete, completing the process from data collection to visualization, the cloud platform’s construction, and finally a full energy management platform. Various machine learning methods are applied to energy management by predicting the chilled water energy meter return temperature of the central air-conditioning system and comparing its performance. Among the various types of regression algorithms, the mean-square error (MSE) of decision tree regression is 0.36, the MSE of support vector regression (SVR) is 0.09, the MSE of K-nearest neighbor (KNN) regression is 0.57, and the MSE of extreme gradient boosting (XGBoost) regression is 0.32. The SVR, the XGBoost regression, and the decision tree regression perform better in various indices. Full article