Search Results (85)

Almería’s high-yield Mediterranean agricultural model is based fundamentally on eight main greenhouse vegetables, with tomato, peppers, and cucumber identified as the principal crops. To facilitate regional diversification, the biological adaptation and techno-economic viability of Vanilla planifolia are examined. The physiological requirements of this orchid, an obligate CAM plant, regarding light intensity and water-use efficiency highlight the need to implement active heating systems, a double internal roof, high-density shading, and misting to emulate the tropical conditions required for this crop in Mediterranean-climate greenhouses. Globally, the market value fell from 898 million USD in 2020 to nearly 328 million USD in 2025, revealing extreme price volatility. Around 75% of global production is absorbed by European and North American markets. While primary production is concentrated in Madagascar, Indonesia, Uganda, and Papua New Guinea, strategic commercial roles are maintained by traders in non-producing nations such as France, the Netherlands, and Germany. Between 2022 and 2024, a systemic collapse was recorded in export revenues. During this period, mean prices fell from 130.83 USD/kg to 44.57 USD/kg. In 2025, wholesale prices rose slightly to 48.59 USD/kg, and global vanilla demand stands at around 6760 t, a volume similar to that of previous years. Viable cultivation of Vanilla planifolia in southeastern Spain and processing it into cured pods are favored by its geographical proximity to European markets. This strategic positioning can ensure optimal harvest maturity and achieve superior biochemical quality through the incorporation of agro-industrial procedures. This information will provide the knowledge necessary to design a field trial aimed at validating Vanilla planifolia as a crop of interest for Mediterranean greenhouses. Full article

Leakage from water distribution networks (WDNs) are one of the most persistent inefficiencies in urban water management. Global non-revenue water (NRW) volumes have remained broadly stable over two decades despite major technical investment in detection, pressure management, and pipe deterioration modeling, implying a structural obstacle that technical solutions alone cannot resolve. This review argues that the underlying reason is conceptual. Leakage has been treated predominantly as an engineering problem. In reality, it is the visible outcome of three interacting failure domains: physical infrastructure deterioration, climate-driven stress amplification, and governance and institutional inadequacy. No existing review integrates all three domains into a unified diagnostic framework. This paper introduces the Three-Domain Systems Failure Framework (TDSFF) as a structured diagnostic approach to the root causes of WDN leakage. A structured literature review of 58 peer-reviewed and authoritative sources, including gray literature, is presented. Eight comparative tables and five original figures synthesize evidence across global contexts from high-income OECD networks to Sub-Saharan African utilities. An operational four-step classification protocol enables practitioners to apply the TDSFF directly to real utility contexts. The framework and the protocol are presented as proposals. Key findings are as follows. Infrastructure deterioration is necessary but insufficient as an explanation for observed leakage levels. Climate change is increasingly observed to accelerate pipe failure rates through soil movement, thermal cycling, and altered pressure dynamics, with impacts underrepresented in current engineering design practice. Governance failures, including deferred maintenance cycles, absent regulatory NRW targets, and chronic underinvestment, explain the persistence of leakage independent of physical deterioration. Technical solutions achieve limited system-level impact when governance preconditions for sustained management are absent. The TDSFF is offered as a practitioner-facing diagnostic aid to identify the dominant failure domain and direct investigation accordingly. Relationships between the domains rest on observational evidence and are reported as associations, not as estimated causal effects. Full article

Non-Revenue Water (NRW) from leakage represents a major global economic and environmental challenge for urban utilities. While Deep Learning (DL) offers transformative potential for leak detection in Water Distribution Systems (WDSs) and existing reviews provide critical assessments, a consolidated, quantitative evaluation of real-world applicability and performance consistency that is actionable for engineering practice remains absent. This systematic review critically evaluates DL applications for WDS leak detection and localisation, with a focused analysis of model accuracy in relation to data types, methodological rigour, and the validation gap between controlled experiments and operational deployment. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework, a systematic literature search was performed using Scopus, Web of Science, Google Scholar, ScienceDirect, Taylor & Francis, and MDPI databases for publications spanning the period from 2015 to 2025. From an initial 5265 records, 72 studies met the inclusion criteria for qualitative synthesis. Analysis revealed a specialisation of DL architectures by data modality: Convolutional Neural Networks (CNNs) applied to acoustic or vibration data yield the highest reported accuracy for direct leak identification; Long Short-Term Memory (LSTM) and Transformer models are predominant for temporal hydraulic data (pressure and flow); and Graph Neural Networks (GNNs) excel with topological data for state estimation. While reported accuracy is often high, performance is highly contingent on data quality and pre-processing. A significant disparity exists between results on synthetic versus real-world validation datasets, ranging from a decline of approximately 3 to 30 percentage points, with reported real-world accuracy spanning 70 to 79.7 percent. Moreover, DL demonstrates a paradigm shift in technical capability for leak management. However, transitioning to reliable field applications requires overcoming key challenges: standardising benchmarks and performance reporting, improving model generalisability and explainability, and fostering integration within practical Digital Twin (DT) frameworks to enable proactive infrastructure management. Full article

Definitions of Non-Revenue Water (NRW), the Economic Level of Leakage (ELL), and their components are well-established and extensively documented in the literature. However, their practical calculation is often complex, typically requiring data that may not be readily available, or which must be estimated. In particular, the methodology used to determine the ELL is frequently subject to debate. All water supply systems (WSS) seek to minimize water losses, but the key challenge lies in identifying the optimal level of loss reduction. Beyond purely financial considerations, leakage reduction activities can have significant operational, social, and environmental implications for WSS. The broader environmental context in which a WSS operates can also shape the acceptable level of NRW. In this paper, the Acceptable Leakage Level (ALL) is defined as the level of NRW that is considered acceptable for a given WSS within its societal context, but is not universally quantifiable. The ALL can differ from the ELL, and this paper presents examples from Serbia and other countries. Factors influencing ALL are also discussed, including the underlying reasons for its divergence from ELL. Full article

This discussion paper addresses the financial and regulatory challenges of implementing Full Water Cost recovery in urban systems with high Non-Revenue Water under climate variability. Using a pipe network in Kozani, Greece, as a case study, the analysis quantifies Direct, Environmental, and Resource Costs across the water cycle. A novel indicator, the Minimum Charge Difference (MCD), reveals distortions caused by fixed charges. The framework integrates pricing, demand elasticity, and hydraulic management. Additionally, a per capita tariff approach is introduced under revenue neutrality, enabling evaluation of distributional impacts and revealing inequities of connection-based pricing, providing a practical tool for socially fair tariff design and policy implementation. Full article

The optimal deployment of pressure monitoring sensors in water distribution networks is crucial for leak detection, network calibration, and system diagnostics. Water utilities face increasing pressure to reduce non-revenue water losses while continuing to improve service quality under budget constraints, thus making the strategic deployment of sensors a critical priority. However, traditional optimization approaches come with various disadvantages including high computational complexity, limited scalability, or dependence on uncertain preliminary parameter estimates. This paper addresses these shortcomings by proposing an innovative integrated framework that balances topological and hydraulic considerations, and applying a flexible metric blending approach to enable robust sensor positioning across networks that differ in scales and topologies. The methodology has been validated through three case studies: a theoretical reference grid, an urban district network, and a large-scale multisource irrigation system. The results prove the methodology to be consistently effective in identifying optimal sensor configurations across all test cases. Full article

Efficient and sustainable management of urban water resources is challenged by increasing water demand and water losses in distribution networks. Developing a Standard Water Balance (SWB) at the District Metered Area (DMA) scale is an effective method for quantifying water losses and supporting Non-Revenue Water (NRW) reduction strategies. This study applies a monthly SWB to a pilot DMA in Antalya, Türkiye. System input volumes were obtained from SCADA-based flow measurements, while billed authorized consumption was calculated using customer billing records from the local water utility. The results show distinct monthly variations in system input volume, with higher water losses observed during periods of increased demand. These findings demonstrate that a monthly, DMA-based SWB is a practical decision-support tool for sustainable water loss management. Full article

An EU-funded project was implemented to enhance efficiency and reliability in the water supply system of Antalya city in Türkiye to support climate change adaptation by reducing Non-Revenue Water (NRW). Extensive fieldwork and targeted actions of continuous Minimum Night Flow monitoring, Active Leakage Control, pressure management, and replacement of aging meters were applied to identify and reduce NRW. The project demonstrated that the commonly used percentage water loss indicator in Türkiye, the regulatory performance indicator, is biased and that the Infrastructure Leakage Index provides a more accurate performance measure. Training and experience-sharing workshops were conducted for district, provincial, and metropolitan municipalities in addition to an international regional conference, strengthening institutional capacity for sustainable water loss management. The project demonstrated that substantial gains in efficiency, reliability, and climate resilience can be achieved through integrated water loss management, advanced monitoring technologies, and performance-based evaluation frameworks. Full article

Introduction: Professional inland fisheries in Portugal remain poorly characterized despite their ecological, social, and territorial relevance. Objective: The objective of this study is to examine the evolution of the biomass catched by inland professional fisheries and determine its economic value. Methodology: This study examines the evolution of declared biomass between 2012 and 2024 and estimates the market relevance of this activity using official catch declarations submitted to the national licensing authority (ICNF). Records were harmonized by species and water body and subsequently aggregated at hydrographic basin level to identify long-term temporal and spatial patterns. Economic estimation was based on a gross production approach combining declared biomass with species-specific price information collected from retail channels and reports from professional fishermen. Changes in species composition were also analyzed to assess whether the observed trends reflect a broader restructuring of freshwater exploitation. Results: The results show a marked interannual variability and a strong spatial concentration of catches, with a limited number of basins (international rivers) accounting for most reported biomass. They also reveal the increasing prominence of non-native taxa in total catches; particularly, the red swamp crayfish, while native migratory species, although represented by lower volumes, maintain high unit prices and make a relevant contribution to total revenue. This contrast suggests that recent changes in freshwater catches are not merely quantitative, but also structural, with implications for ecological status, the growing dependence of the fishery on invasive species, and the territorial distribution of economic returns. Conclusions: By combining official catch declarations with market-based valuation, this study provides an updated overview of the recent evolution of professional freshwater exploitation in Portugal and offers a useful basis for fishery governance, monitoring programmes, and future discussions on conservation, licensing, and basin-scale management. 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

Non-revenue water (NRW) entails serious problems for water distribution networks (WDNs), including contamination, leakage, unauthorized use, and inefficient invoicing. These issues lead to large financial losses and operational inefficiencies. This work investigates and focuses on Internet of Things (IoT) and Artificial Intelligence (AI) technologies involving WDNs that could be employed for monitoring NRW distribution. According to the analysis, NRW resulting from contamination, unauthorized connections, and unpaid water bills causes water companies to lose a substantial amount of money. In the water industry, the implementation, utilization, and installation of IoT–AI technologies can help decision-making, improve sustainable development, develop innovative products and services, and find solutions. Furthermore, IoT technologies and protocols can assist the water sector in reducing NRW, improving WDN management and operations, and identifying key challenges, including sensor reliability, communication constraints, cybersecurity concerns, scalability issues, and cost-effectiveness in practical deployment. This study offers an integrated analysis of IoT technologies, AI techniques, communication protocols, and NRW management strategies within a unified WDN perspective to earlier review articles that independently concentrate on leakage detection, IoT frameworks, or AI applications. Lastly, the study’s originality depends on its ability to show how theoretical advancements and technologies can be commercialized in cost-effective smart water distribution systems, contributing to the advancement of resilient urban water infrastructure and smart city development. Full article

The blue economy contributes significantly to Brazil’s gross domestic product due to the country’s vast coastline and abundant natural resources. Oil royalties represent a major component of this wealth; yet, their association with improvements in quality of life remains unclear. The aim of this study was to analyze the performance of 193 municipalities (coastal: 101; state of Rio de Janeiro: 92) that receive more than R$5 million in royalties per semester in 2022, using the socioeconomic indices IBP (Brazilian Deprivation Index), IDEB (Basic Education Index), and IQA (Water Quality Index). The results reveal conditional, non-linear, and regionally unequal relationships between oil revenues and socioeconomic indicators. Unsupervised learning identified four groups of municipalities. The group with the largest number of municipalities (n = 45) and the best performance in socioeconomic indices had a wide range of royalties (between R$7 and R$22 million). However, supervised analyses show that this group of municipalities, mainly from the south/southeast regions, receives relatively low oil revenues but performs well in the indices, suggesting a certain autonomy in relation to royalties. The municipalities of the state of Rio de Janeiro confirm the national trend, with cities with higher education levels benefiting, but with more specific aspects of the blue economy (water quality) not being well-represented. Policies are mandatory to redirect oil revenues to these sectors with the support of more appropriate indicators. Full article

Semi-arid territories in Latin America face chronic water stress; limited observability and fragmented institutions constrain effective water resources management (WRM). This narrative review synthesizes peer-reviewed evidence (2020–2026) on smart technologies that strengthen basin- and utility-level WRM, using Peru (Piura-like coastal semi-arid contexts) as an anchor and Latin America as a comparative lens. We used a structured, traceable database-based workflow and synthesized studies reporting measurable outcomes across five application categories: drought/flood early warning, hydrometeorological forecasting, water quality surveillance, non-revenue water (NRW)/leakage, and allocation and compliance. Findings were organized into an application-oriented taxonomy spanning remote sensing (RS) and GIS, Internet of Things (IoT)/telemetry, analytics/AI-enabled decision support, and hybrid approaches. Evidence most consistently reports operational gains (coverage, timeliness, predictive performance), while governance outcomes are less frequently measured and appear contingent on interoperability, digital capacity, and sustainable operations and maintenance (O&M) conditions. We conclude with a territorial adoption agenda specifying minimum enabling conditions and a phased pathway from pilots to scalable, eco-efficient smart WRM in Peru and comparable semi-arid settings across Latin America. Full article

Water systems experience increasing sustainability challenges from climate variability, aging infrastructure, and energy and chemical intensity demands, but AI has typically been assessed against prediction accuracy rather than demonstrated operational success. This PRISMA 2020 systematic review analyzed the role of AI solutions on sustainability in distribution, treatment, and basin management. The database search identified 920 records; after deduplication (n = 185), screening was conducted on n = 735 titles/abstracts and examination of the full text for n = 85, providing a total of n = 41 included peer-reviewed studies for qualitative synthesis and n = 38 for quantitative/bibliometric synthesis with the additional analysis of seven grey-literature sources. Evidence mapping reveals high growth post-2020, and distribution and wastewater operations are dominated by a few companies. The most deployable evidence is found with monitoring, anomaly/leak detection, and short-term forecasting, while optimization and reinforcement-learning control are primarily simulation validated with limited field applications. While accuracy metrics are often reported, transformation into water saved, kWh/m³, chemicals, compliance/reliability/resilience/equity measures are inconsistently and less frequently operationalized. In general, AI is most believable when it is part of analysis-ready workflows, bounded decision support, and measurement-and-verification. Full article

Water utilities continue to lose a lot of Non-Revenue Water (NRW) because of leaks that go undetected. This makes it necessary to find accurate but easy-to-use monitoring solutions. This paper presents FiT-WST+, a wavelet-guided Frequency-Informed Transformer (FiT) designed for the classification of five distinct leak types utilising accelerometer measurements. The proposed architecture combines the spectral modelling ability of a FIT with the stable translation-invariant representation of the Wavelet Scattering Transform (WST). The model uses a guided attention mechanism to combine spectral and scattering cues that work well together to make classes more distinct, especially for fault types that are similar. On the held-out test set, FiT-WST+ achieves 99.6% accuracy, 99.6% balanced accuracy, and a 99.6% macro-averaged F1-score. Comparative benchmarking against recent methods tested on the same dataset shows that this method works at a low sampling rate (1 kHz), which greatly lowers bandwidth needs and allows for scalable deployment on edge devices with limited resources for real-time monitoring of important water infrastructure. Full article