Search Results (260)

Intelligent completion technologies, including Inflow Control Valves (ICVs), have become increasingly important for remotely managing zonal production in complex well architectures. However, quantifying flow rates and phase fractions in such systems remains challenging due to space constraints and the harsh downhole environment, which limit the deployment of conventional sensors. Distributed Acoustic Sensing (DAS) provides a promising solution by converting standard fiber-optic cables into dense arrays of acoustic sensors. While DAS has been successfully applied in applications such as integrity monitoring and leak detection, its use for direct two-phase flow characterization within intelligent completions remains largely unexplored. In this study, we present a DAS-based methodology to monitor and analyze oil–water two-phase flow in horizontal experiments that mimic field conditions. Acoustic data collected from DAS are transformed into time–frequency spectrograms using Short-Time Fourier Transform (STFT) to extract dynamic spectral features. These features are then correlated with pressure drop across the ICV and flow rate, revealing distinct frequency band behaviors associated with fluid changes. To quantify flow characteristics, a power-law model is trained using spectral features to predict flow rate and phase fractions. The results demonstrate strong predictive capability for pressure drop and flow rate under controlled laboratory conditions, highlighting the potential of DAS for multiphase flow diagnostics in field applications with intelligent completions, while water cut prediction remains challenging due to the complex and non-unique relationship between flow conditions and DAS response and is left for future work. This research not only provides new insights into the acoustic response of oil–water flows but also introduces a data-driven framework for leveraging DAS in real-time flow monitoring and control within ICV-equipped completions. Full article

This paper presents an innovative asset management system developed by ISOIL to predict pipe failures and reduce non-revenue water losses in distribution networks. The system combines advanced risk assessment algorithms with mobile data collection tools to identify critical pipeline sections and optimize replacement strategies. Applied to a medium-sized utility in Northern Italy, the approach successfully identified 2.36% of the network (~38 km) with the highest vulnerability levels. The predictive model demonstrated 68% accuracy in identifying locations where new leaks subsequently occurred, validating its effectiveness for proactive maintenance planning and leak detection optimization. Full article

Reliable monitoring of hydraulic networks is essential for efficient and sustainable water management in agriculture. To address the growing need for intelligent, low-latency anomaly detection in such systems, we propose HydroNeuro, a domain-aware embedded framework that integrates hydraulic domain knowledge with data-driven neural inference for the real-time detection of leaks and obstructions. Rather than embedding physical equations directly into the learning objective, we leverage established hydraulic principles, including Bernoulli’s equation and the Darcy–Weisbach formulation, to structure the experimental design, interpret pressure–flow relationships, and ensure physical consistency of the learned representations. These principles confirm that pressure deviations induced by leaks or obstructions are causally explainable and measurable. We employ a fractional factorial design (FFD) to optimize valve activation combinations and sensor configurations during dataset acquisition, thereby reducing redundant experiments, water circulation, and energy consumption while limiting mechanical stress on system components. We deploy a lightweight neural network on an ESP32 microcontroller using TensorFlow Lite for Microcontrollers to enable energy-efficient, low-latency edge inference under severe hardware constraints. Our experimental validation on a laboratory-scale hydraulic testbed demonstrates anomaly detection accuracy exceeding 96%, with strong robustness under sensor noise and hydraulic perturbations. Compared to a multiple linear regression baseline, the proposed neural model reduces the prediction error from an RMSE of 0.58 to 0.12. By coupling physically consistent experimental modeling with embedded neural inference, HydroNeuro provides a scalable and practically deployable solution for autonomous hydraulic monitoring in precision irrigation and smart water distribution systems. Full article

Rapid urbanization in Saudi Arabia puts increasing pressure on energy, water, mobility, and waste-management systems, strengthening the need for evidence-based smart-city policy under Vision 2030. Rather than offering a descriptive inventory of projects, this paper develops a verification-oriented framework for assessing smart-city initiatives in the Kingdom. The framework is built on four principles: (i) distinguishing national contextual indicators from city-level evidence, (ii) separating stated ambitions from observed outcomes, (iii) applying an evidence-grading rubric that prioritizes publicly verifiable mechanisms and performance indicators over anecdotal or promotional claims, and (iv) introducing a readiness–impact matrix adapted to Saudi climatic, infrastructural, and institutional conditions. The framework is applied to major Saudi smart-city cases, including NEOM, KAEC, Riyadh, Jeddah, Makkah, and Madinah. The analysis shows that the strongest publicly documented evidence is concentrated in selected sectoral applications, particularly demand response and smart-building control in electricity systems, leak detection and pressure management in water networks, and intelligent traffic management in urban transport. These cases indicate plausible pathways for improving service efficiency and reducing resource waste; however, publicly verifiable city-level outcome data remain limited, fragmented, and uneven across cases. In response, the paper proposes a policy playbook centered on KPI transparency, interoperable data governance, cybersecurity safeguards, and public–private partnership templates to improve the measurability, comparability, and scalability of smart-city outcomes. By formalizing verification and cross-case assessment, the study contributes a reproducible methodological basis for evaluating smart-city progress and prioritizing future investments in Saudi Arabia. Full article

Water distribution systems are critical infrastructures, and are highly susceptible to a wide range of anomalies like leaks and component failures. Hence, timely detection of abnormal system behavior is essential for their safe and efficient operation. To address this challenge, we generated synthetic hydraulic datasets to train a machine learning tool, tailored for anomaly detection and classification tasks. The proposed architecture integrated bidirectional gated recurrent unit layers with time-distributed dense layers employing Rectified Linear Unit activations, enabling the extraction of temporal dependencies alongside spatial feature representations. The strong performance achieved highlights the robustness of the approach in distinguishing between normal operating states and heterogeneous anomaly classes, demonstrating its potential for enhancing system reliability. Full article

Leaks in pipe systems result in significant economic losses, environmental hazards, and public health risks. Transient-based leak detection methods, which exploit the dynamics of pressure waves in response to system anomalies, have emerged as efficient techniques for identifying and characterizing leaks in pressurized pipelines. These methods offer distinct advantages, including minimal data requirements, high sensitivity to low-pressure anomalies, and resilience to the ill-posed conditions often affecting steady-state models. This paper reviews transient-based leak detection, synthesizing findings from over 139 peer-reviewed publications spanning the past three decades. The review categorizes transient-based methods into transient damping, transient reflection, system response, and inverse transient methods, analyzing the prevalence, evolution, and research rate of each category over time. By structuring the review around key aspects such as simulation domain type, analysis approach, system response, solver strategies, adaptability to noise, viscoelasticity, and network complexity, this paper identifies significant trends and shifts in research focus. A comprehensive tabular dataset of 139 studies captures how research activity in various areas has accelerated, slowed, or reached stability, offering insights into the evolving priorities within the field. This review highlights areas for further development, particularly in addressing AI-enhanced applications, transient excitation and measurement sites design, noise resilience, comprehensive leak characterization, validation approaches, and scalability for complex network applications, providing a resource to guide future research in transient-based leak detection. Full article

South African irrigation schemes face critical challenges of water scarcity, infrastructure deterioration, and limited monitoring capacity, threatening agricultural productivity and food security. This scoping review systematically analyses 59 peer-reviewed publications (2000–2025) on sensor-based and acoustic irrigation monitoring technologies in South Africa, using transformer-based natural language processing (Sentence-BERT embeddings), unsupervised Machine Learning (UMAP dimensionality reduction, HDBSCAN clustering), and geospatial mapping applied to literature retrieved from Web of Science and Scopus. Results show that water quality monitoring (42.4% of studies) and remote sensing (25.4%) dominate the national research landscape, while soil moisture sensing and modelling remain comparatively limited. Notably, no peer-reviewed studies applying acoustic monitoring technologies to irrigation were identified, representing a critical gap despite proven international applications for leak detection (95–98% accuracy), widespread infrastructure aging (over 50% of schemes exceeding 30 years), and reported water losses of 30–60% in poorly managed systems. Reported experimental water savings range from 15% to 30%, yet applications remain largely confined to pilot-scale implementations concentrated within a limited number of Water Management Areas. Persistent adoption barriers include infrastructure unreliability, financial inaccessibility, limited digital literacy, and weak institutional coordination. The review recommends: (i) expanding research coverage across underrepresented regions and Water Management Areas; (ii) strengthening extension support and technical training to enable broader adoption; and (iii) integrating low-cost sensor networks with predictive, data-driven irrigation advisory systems. These priorities aim to support scalable, context-sensitive irrigation modernisation under increasing water scarcity pressures. Full article

The increasing scarcity of urban water resources, particularly in regions with intermittent supply and household water storage tanks, demands monitoring approaches capable of identifying end-use consumption patterns beyond aggregated volume measurements. Framed primarily as a feasibility study, this research presents an IoT-based framework for the automated classification of residential water consumption activities using water-level dynamics and supervised machine learning. A non-intrusive sensing architecture based on hydrostatic pressure measurements was deployed in a domestic water tank and integrated with a cloud-based data acquisition and processing platform. Five representative household states and activities were considered: tank refilling, stable state, toilet flushing, washing clothes, and taking a bath. A labeled dataset comprising 4396 consumption events was used to train and evaluate Decision Tree, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors, and Recurrent Neural Network (LSTM) models using features derived from water-level variations. All models achieved high performance, with accuracies above 0.92 and weighted F1-scores up to 0.93. The evaluated models showed highly comparable results, with the SVM (RBF) achieving a slightly higher accuracy (0.9307) in this evaluation setting, while ROC analysis showed AUC values between 0.97 and 1.00 across all classes, indicating strong discriminative capability. Additionally, specific activities such as washing clothes and tank refilling achieved precision and recall values above 0.95. These findings confirm that hydrostatic pressure-based sensing, combined with machine learning, enables reliable identification of domestic water-use events under intermittent supply conditions. The proposed approach provides actionable insights for demand management, leak detection, and user awareness, supporting more efficient and sustainable residential water consumption strategies. Full article

A water company’s water supply network is responsible for distributing good-quality water in quantities that meet customer needs, ensuring proper operation of the water supply network to ensure adequate pressure at the receiving points, efficiently repairing faults, and planning and executing maintenance, modernization, and expansion work. Managing a water supply network is a complex and complex process. A crucial challenge in water company management is detecting and locating hidden water leaks in the water supply network. Leak location in water distribution networks is a key challenge for utilities, as undetected leaks lead to water losses, increased energy consumption, and reduced service reliability. With the development of cyber-physical systems (CPSs), the integration of physical infrastructure with real-time digital monitoring has enabled more adaptive and responsive water operations. Data-driven decision-making in CPS in the water industry leverages classification and regression trees (C&RTs) to analyze real-time sensor data—such as pressure, flow, and consumption—to classify system states and predict potential faults. By transforming operational data into interpretable decision rules, C&RTs enable automated and timely maintenance actions that improve reliability, reduce water loss, and support intelligent infrastructure management. The aim of this study is to develop and evaluate AI-based optimization methods to enhance sustainability, efficiency, and resilience in the water industry by enabling autonomous, data-driven decision-making within CPSs, supporting smart industry practices, and addressing practical challenges associated with the actual implementation of smart water management solutions using simple solutions such as C&RTs. The accuracy of the best classifier was 86.15%. Further research will focus on using other types of decision trees that will improve classification accuracy. Full article

We propose and experimentally validate an automatic urban water leakage detection architecture that leverages dark fiber links already deployed in telecommunication networks in underground conduits in the vicinity of water pipelines. The sensing stage relies on a differential-phase coherent optical time-domain reflectometry interrogator enhanced with optical pulse compression to improve sensitivity. Building on this vibration acquisition stage, automatic leakage detection algorithms are implemented by searching for leak-induced activity in the frequency domain, which is well suited to revealing leakage-related features. After acquiring a baseline calibration to characterize normal-condition vibrations at each sensing position, leakage candidates are identified by comparing distribution-based metrics computed over multiple measurements against the corresponding baseline statistics. Two automatic leakage detection strategies are developed. First, low-complexity feature-based metrics are implemented, enabling continuous monitoring with minimal computational requirements. Second, an autoencoder-based anomaly detection technique is introduced, which also relies on location-specific normal-condition calibration but reduces the dependence on prior knowledge of the expected leakage vibration signatures. A real-world field trial on an urban network demonstrates reliable detection and localization using controlled leak events generated in the field, with measurements performed over a 17 km sensing fiber and an effective spatial resolution of 2.6 m. Benchmarking against a commercial punctual electro-acoustic leak detector yields consistent trends. Overall, the proposed system could complement existing technologies by enabling automated, continuous city-scale monitoring over already deployed dark fiber infrastructure. Full article

Methane (CH₄) emissions in urban areas remain a major source of uncertainty in greenhouse gas inventories, particularly in Eastern European cities, where observational studies are limited. This study presents a comprehensive, system-based assessment of CH₄ sources in Cluj-Napoca, Romania, based on high-resolution in situ measurements across five representative urban systems: aquatic environments (AQs), natural gas distribution end-use points (NG), sewer infrastructure (SE), building basements (BSs), and traffic emissions (TEs). Elevated CH₄ concentrations were consistently detected across all investigated systems, confirming the coexistence of both diffuse and point sources within the urban environment. Dissolved methane (dCH₄) in aquatic systems showed strong and persistent oversaturation relative to atmospheric equilibrium, reaching up to 3 × 10⁵% of air–water equilibrium, indicating active microbial methanogenesis enhanced by urban inputs of organic matter and nutrients. Measurements at natural gas end-use points revealed highly localized leaks with concentrations up to 482 ppmv. Sewer infrastructure exhibited extreme variability (up to 1222 ppmv), likely controlled by a combination of microbial production, hydraulic conditions, and potential interactions with adjacent gas distribution networks. Basement environments showed CH₄ accumulation up to 12 ppmv, reflecting the combined effects of gas leakage and limited ventilation. Measurements at vehicle exhausts identified transient CH₄ peaks reaching 162 ppmv during vehicle engine acceleration, with distinct ethane-to-methane ratios, indicative of pyrogenic sources. Overall, these results demonstrate that urban CH₄ emissions are spatially heterogeneous, temporally variable, and derived from multiple coexisting sources. The urban area should, therefore, be understood as a hybrid environment, with natural and anthropogenic CH₄ contributions. Full article

Early leak detection in water distribution networks is essential to minimize losses and improve operational efficiency. This systematic review analyzes 53 studies published between 2018 and 2025 that employed machine learning, deep learning, and hybrid approaches. The results show that pressure is the most widely used and most sensitive input variable for identifying hydraulic anomalies. Most datasets originate from EPANET-generated simulations, while experimental and field data are less common due to their high costs and operational complexity. Machine learning models, particularly SVMs, achieve accuracies between 94 and 100%, demonstrating stability with noisy data and low computational cost, while in deep learning, CNNs are most effective for multiclass classification and localization, typically reaching 95–99% accuracy. Hybrid approaches that combine automatic feature extraction (e.g., CNNs or autoencoders) with conventional classifiers (such as SVMs or LSSVMs) yield the best results, surpassing 97% accuracy and achieving localization errors below 0.2 m. Based on these findings, a theoretical model is proposed using a hybrid CNN + SVM approach to enhance accuracy, robustness, and adaptability in real-time monitoring systems. Full article

This paper investigates the specific positioning accuracies and uncertainties associated with the measurement of acoustic leakage noise correlation (LNC) in underground pressurized water mains, treating them as acoustic waveguides. It begins by identifying three key intrinsic sources of measurement errors: (1) the speed of acoustic waves in the water mains as influenced by pipe material, wall thickness, modulus of elasticity, and bulk modulus; (2) the distance between the two accelerometers used for correlation; (3) the time delay from the point of leakage to the accelerometers. A mathematical uncertainty model was developed to compute sensitivity coefficients, enabling the propagation of measurement errors from these sources. This was validated through seven sets of full-scale experiments conducted at Q-Leak, a 25,000 sq. ft. test site in Hong Kong. This study ultimately quantified and assessed the contributions of individual error sources to the overall uncertainty, allowing for the prioritization of factors that have the most significant impact in various scenarios. The findings reveal that Young’s modulus and pipe wall thickness are the primary factors affecting measurements for both plastic and metal pipes. Additionally, a universal in-house program, “LNC uncertainty calculator,” was developed to provide insights into the buffer ranges for confirming suspected leak locations while considering constraints within the uncertainty budget. This research highlights the critical but often overlooked area of uncertainty modeling in leak detection for pressurized buried water mains, offering valuable insights intended to enhance operational strategies and maintenance practices within the industry. This research provides a robust framework for understanding the accuracy of leak detection. This means operators can better interpret the reliability of their measurements, leading to consistent decision-making across different situations and minimizing the risk of misidentifying the presence or absence of leakage. In addition, the insights gained from prioritizing factors that affect measurement accuracy allow engineers and operators to make informed decisions about where to focus their resources and efforts. This can lead to more effective maintenance strategies that are tailored to specific conditions, thereby optimizing operational efficiency. Full article

Leakage detection in water distribution networks plays an instrumental role in effectively addressing water loss, yet the scarcity of annotated leak events limits the applicability of supervised classification methods. While hydraulic simulation-generated datasets are often considered as an alternative, their generation is hindered by incomplete network topology and sparse sensor coverage in real-world settings. Consequently, many real-world solutions rely on unsupervised anomaly detection approaches but frequently struggle to balance sensitivity and accuracy. This study proposes a regression-ensemble framework that learns the district metered area (DMA)-specific demand–supply dynamics to detect emerging leaks using smart meter data, without requiring real or simulated labeled leak datasets for training. Regression models—Random Forest, Support Vector Regression, XGBoost, and Multi-Layer Perceptron—are trained on DMA-level consumption and supply data that are preprocessed to preserve background leakage while correcting emerging leaks. Deviations between predicted and observed supply are quantified through Pearson correlation, Kendall’s tau, and Z-score, whose anomaly indications are combined at metric and model levels using weights derived from model prediction accuracy. A leak is identified once the ensemble anomaly score crosses a threshold. The system detects leaks within 8–12 h of onset, achieving 90% and 98% accuracy on simulated and real leak scenarios, respectively, at an anomaly-score threshold of 0.5. Recall rates of 85% and 95% are observed for simulated and real leaks, respectively, whereas 95% and 100% recall are observed for no-leak events in both leak scenarios, respectively. Our proposed framework demonstrates the potential of smart meter-driven ensemble analytics for rapid and robust leak detection. Full article