Search Results (114)

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

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

Landslides present remarkable hazards in the Himalayan region, particularly in areas with young and fragile topography. Mitigating vulnerability requires assessing susceptibility, which relies heavily on the accuracy of susceptibility maps generated through various approaches that consider different conditioning factors at various resolutions. This study, conducted in Jajarkot District within the Karnali Province of Nepal and covering 2230 km², aims to identify suitable conditioning factors at appropriate resolutions. Sixteen factors, encompassing topography, hydrology, geology, and anthropogenic activities, were analyzed alongside a landslide inventory of 159 occurrences compiled from satellite imagery, the literature, and field surveys. A genetic algorithm (GA) was employed to determine the optimal set of conditioning factors, while Maximum Entropy (Maxent) modeling produced landslide susceptibility maps (LSM) at spatial resolutions ranging between 12.5 and 200 m. Resolution selection was guided by Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) analyses. Multicollinearity testing identified 15 influential factors, with land use ranking highest at 22.7%, followed by stream power index (SPI), drainage density, and aspect. The GA consistently highlighted land use and slope as effective factors across subset sizes. The results indicated resolutions finer than one hundred meters enhanced discrimination between landslide and non-landslide areas, emphasizing the need to balance resolution with computational resources and data availability. This study emphasizes the intricate interplay of conditioning factors, the GA’s efficacy in subset selection, and the crucial role of resolution in the improvement of susceptibility models. The findings provide practical insights for policymakers and disaster management authorities, aiding evidence-based decision making in the mitigation of landslide risk in Jajarkot and similar regions. Full article

Detailed hydraulic modeling of a water distribution network (WDN) in an urban area is implemented therein, based on data from geoinformatic tools (GIS), to investigate and analyze the network’s hydraulic response to different scenarios of operation. A detailed mapping of the water meters of the consumers in the urban district is therefore conducted in the frame of a District Metered Area (DMA) zoning. Different consumptions according to water meters and patterns of daily water demand, resulting from both theoretical and measured data from a limited number of smart meters, are used in the hydraulic simulations. The analysis conducted assists common engineering practice to identify critical locations for constructing new hydraulic infrastructure, resulting in the restructuring and reorganization of the DMA, assisting to face existing and common problems of WDNs within the general framework of DMA design and efficient water management. A case study on the WDN of Efkarpia, located in the city of Thessaloniki, Greece, satisfying the principal design criteria of DMAs, is presented in this work, under both normal and emergency conditions. Hydraulic analysis is performed based on different scenarios, mainly consisting of different consumptions according to water meters and different demand patterns, all resulting in high pressures in the southern part of the DMA. Hydraulic simulations are then performed considering two basic operating scenarios, namely the operation of the old DMA of Efkarpia and a new DMA, which is reduced in size. The two scenarios are compared in terms of estimated pressures in the studied area, as well as in terms of energy consumption in the upstream pumping station. The comparisons reveal that the new DMA outperforms the old one, with a large increase in the pressure at nodes where low pressures were assessed in the old DMA, a reduction in daily pressure variation up to 45%, and quite significant energy savings assessed around 21.6%. Full article

Accurate regional crop classification, acreage estimation, yield prediction, and crop water requirement assessment are essential for effective agricultural planning and market forecasts. This study uses an integrated geospatial and statistical approach to assess major winter crops wheat and sugarcane cultivation in Ghotki District, Pakistan, from 2017/18 to 2022/23. It combines satellite data from Landsat 8 and Sentinel-2, ground truthing, and crop reporting records to analyze key factors such as cultivation area, crop gradients, vegetation health, normalized difference vegetation index (NDVI)-based wheat and sugarcane yield models, crop water requirements, and total irrigation water consumption. Results showed that wheat cultivation areas ranged from 15% to 19%, with the highest coverage observed in the 2021/22 winter season. Sugarcane cultivation ranged from 6% to 10%, peaking in the 2018/19 season. A strong linear association between NDVI and wheat yield (R² = 0.86) was observed. Wheat and sugarcane yield predictions utilized linear regression, and robust linear regression models, all of which were validated by the findings. Irrigation water demand for the winter season was calculated at 1887 million cubic meters (MCM) in 2017/18, with 1357 MCM supplied by the Sindh Irrigation Drainage Authority (SIDA). By 2020/21, water demand reached 2023 MCM, while SIDA’s supply was 1357 MCM. These results highlight the significance of integrating geospatial analysis with statistical records to provide timely, reliable estimates for cropped areas, yield forecasting, vegetation dynamics, and irrigation planning. The proposed methodology contributes a scaleable solution for informed decision-making in agricultural and water resource management, applicable across other districts in Pakistan and on a global scale. Full article

Background: Neonatal jaundice (NNJ) remains a leading cause of newborn mortality in much of sub-Saharan Africa. We sought to examine the validity of using a hand-held icterometer as a screening tool to determine which newborns need further assessment. Additionally, we sought to assess the feasibility of its use among mothers. Methods: We recruited and trained healthcare workers at one large district hospital in Ghana to use a hand-held icterometer known as the Bili-Ruler^TM. We recruited mothers of 341 newborns aged 0 to 2 weeks at the same hospital. Mothers watched a standardized training video, after which they blanched the skin of the newborn’s nose and compared it with the yellow shades numbered one to six on the icterometer. Each newborn was also assessed with a transcutaneous bilirubin meter (TCB). Research assistants and health care workers screened the same newborns, recorded their scores separately, and were blinded to each other’s readings. In the second phase of this study, we recruited 100 new mothers to take the Bili-Ruler home with them, instructing them to check their newborns twice daily. We interviewed them 1–2 weeks later to determine the acceptability and feasibility of its use. Results: Out of 341 newborns screened, 20 had elevated TCB indicative of hyperbilirubinemia. Healthcare workers’ Bili-Ruler ratings had a strong and significant correlation with TCB scores, as did the ratings of researchers and mothers. When comparing Bili-Ruler scores against TCB, sensitivity across all three raters was 80% (95% CI 75.6–84.3), specificity ranged from 61.1% (healthcare providers) to 66.7% (researchers), positive predictive value ranged from 11.4% (healthcare providers) to 13.0% (researchers), and negative predictive value was 98.0% or higher across all raters. Area under the ROC curve ranged from 0.71 for healthcare providers to 0.73 for researchers. Mothers AUC was 0.72. In terms of acceptability and feasibility, the Bili-Ruler was widely accepted by the mothers and family. In total, 98% of mothers reported using it, and 90.8% used it 3 or more days in the first week after birth. Moreover, 89.8% used it more than twice per day. Conclusions: A hand-held, low-tech icterometer is an important potential mechanism for improving early jaundice identification in low-resource settings. Further studies using larger sample sizes with a higher prevalence of hyperbilirubinemia are warranted. Full article

The residential minimum water demand characterisation is of fundamental importance for water distribution system management. During the minimum consumption, indeed, maximum pressures are on network pipes, and at the same time, tank levels rise. The water consumption analysis during the period of low demand and high pressure is thus of great interest for leakage estimation due to the increase in water loss with pressure. In order to contribute to the study of and forecast the daily minimum residential water demand, some probability distributions were tested by means of statistical inferences on a data set collected from different District Metering Areas (DMAs), showing that the stochastic minimum flow demand is defined by the Log-Normal (LN), Gumbel (Gu) and Log-Logistic (LL) distributions, as an extreme minimum value. With reference to the analysed DMAs, the parameters of such statistical distributions were estimated and the relationships are provided as a function only of the supplied users for different DMAs. The data were analysed with 1 h intervals of discretisation, with the aim of providing a useful guide to water utilities, which usually manage water distribution system data with such a resolution time. Indeed, once the minimum residential flow consumption at a 1 h interval was estimated as a function of the user number, by subtracting it to the inflow measured, it is possible to estimate the leakages rate at the DMA. Full article

Water management has evolved significantly, but sustainability remains a critical challenge. Ancient Roman aqueducts, despite their engineering marvel, operated with constant flow, leading to substantial water waste. Later, rooftop reservoir systems continued this inefficiency, as excess water would overflow. Only recently have demand-driven networks shown potential for reducing waste, though substantial water leaks continue to undermine these efforts. Achieving true sustainability in water distribution requires minimizing leaks through the use of models that adopt accurate water demand scenarios and identifying an optimal peak factor (PF). In fact, water distribution networks (WDNs) are commonly designed, analyzed, and calibrated using deterministic demand scenarios based on average annual consumption and scaled by a chosen PF. However, for efficient design and management, it is essential to associate a probabilistic value with the consumption data used in the analyses. This study introduces a novel methodology for estimating PFs with a specific return period at the District Meter Area (DMA) scale, utilizing extreme value statistical analysis. The generalized Pareto distribution (GPD) models were applied to provide more reliable PF estimates. The proposed methodology was validated using hourly residential consumption data from a DMA located in Southern Italy, demonstrating its effectiveness in improving the accuracy of WDN design. Full article

The spectral reflectance measured in situ is often regarded as the “truth”. However, its limited coverage and large spatial heterogeneity often make the ground-based reflectance unable to represent the remote sensing images. Since the spatial scale mismatch between ground-based, airborne, and spaceborne measurements, the applications of geological exploration, metallogenic prognosis and mine monitoring are facing severe challenges. In order to explore the influence of spatial scale effect on rock spectra, spectral reflectance with uncertainty caused by differences in illumination view geometry and spatial heterogeneity is introduced into the Bayesian Maximum Entropy (BME) method. Then, the rock spectra are upscaled from the point-scale to meter-scale and to 10 m-scale, respectively. Finally, the influence of spatial scale effect is evaluated based on the reflectance value, spectral shape, and spectral characteristic parameters. The results indicate that the BME model shows better upscaling accuracy and stability than Ordinary Kriging and Ordinary Least Squares model. The maximum Euclidean Distance of rock spectra caused by spatial resolution change is 6.271, and the Spectral Angle Mapper can reach 0.370. The spectral absorption position, absorption depth, and spectral absorption index are less affected by scale effect. For the area with similar spatial heterogeneity to the Huangshan Copper–Nickel Ore District, when the spatial resolution of the image is greater than 10 m, the rock’s spectrum is less influenced by the change in spatial resolution. Otherwise, the influence of spatial scale effect should be considered in applications. In addition, this work puts forward a set of processes to evaluate the influence of spatial scale effect in the study area and carry out the upscaling. Full article

Assessing leakage risks in water distribution networks (WDNs) and implementing preventive monitoring for high-risk pipelines has become a widely accepted approach for leakage control. However, existing methods face significant data barriers between Geographic Information System (GIS) and leakage prediction systems. These barriers hinder traditional pipeline risk assessment methods, particularly when addressing challenges such as data imbalance, poor model interpretability, and lack of intuitive prediction results. To overcome these limitations, this study proposes a leakage assessment framework for water distribution networks based on multiple attention mechanisms and a generative model-based data balancing method. Extensive comparative experiments were conducted using water distribution network data from B2 and B3 District Metered Areas in Zhengzhou. The results show that the proposed model, optimized with a balanced data method, achieved a 40.76% improvement in the recall rate for leakage segment assessments, outperforming the second-best model using the same strategy by 1.7%. Furthermore, the strategy effectively enhanced the performance of all models, further proving that incorporating more valid data contributes to improved assessment results. This study comprehensively demonstrates the application of data-driven models in the field of “smart water management”, providing practical guidance and reference cases for advancing the development of intelligent water infrastructure. Full article

While the Sustainable Development Goals (SDGs) have emerged as the preeminent sustainability framework across various spatial scopes, the reporting and assessment of new urban developments and regeneration projects often remain tethered to sustainability frameworks that lack direct alignment with SDG targets. This paper proposes a framework to integrate SDG reporting within urban regeneration initiatives. This approach leverages existing resources, such as the Joint Research Center’s (JRC) European Handbook for SDG Voluntary Local Report (VLR) and UN-Habitat’s Global Urban Monitoring Framework (UMF), to report potential contributions towards SDG progress. The framework is validated through the case study of Madrid Nuevo Norte (MNN), one of the largest urban regeneration projects currently developed in Europe, located in the northern district of the Spanish capital and encompasses the regeneration of a 3.2 million square meter area. The methodology evaluates MNN potential contributions through a set of indicators based on input–output/outcome–impact framework to track the causal pathways arising from MNN activities. This paper presents an analysis of the methodological framework developed for the MNN SDG report during the project-planning phase, with a focus on evaluating the framework’s capacity to accurately estimate the project’s contributions to the SDGs. Full article

The challenge in water demand forecasting within a Northeast Italy water distribution network (WDN) involves predicting demands across ten distinct District Metered Areas (DMAs) with varying characteristics and demand profiles. This is critical for optimizing system operation in the near future. The available data begins in January 2021, with unknown impacts of post-COVID socio-economic changes, like work-from-home policies. To address this, the team integrates heuristic and Machine Learning (ML) techniques to predict short-term demands and fill data gaps. A heuristic post-processing step, using weighted sums and historical trends, refines these predictions. This approach combines ML with traditional methods with a view to servicing developing nations. Full article

Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). The identification pattern of leakage nodes is correlated to the maximum number of leakage points set with the participation of the DMA-CS algorithm, which provide a more accurate pathway for identifying and predicting the specific pipeline leaks. Full article

Water management is vital for building an adaptive and resilient society. Water demand forecasting aids water management by learning the underlying relationship between consumption and governing variables for optimal supply. In this paper, we propose a week-ahead hourly water demand forecasting technique based on deep learning (DL) utilizing an encoded representation of historical supply data and influencing exogenous variables for a District Metered Area (DMA). We deploy a CNN model with and without attention and evaluate the model’s ability to forecast the supply for different DMAs with varying characteristics. The performances are quantitatively and qualitatively compared against a baseline LSTM. Full article

This paper introduces a two-step methodology for short-term water demand forecasting. In the first step, a pre-processing analysis of the inflow input data is conducted to evaluate completeness and quality, ensuring optimal data integrity. Subsequently, in the second step, a robust machine-learning algorithm is employed to predict the water demand patterns. The methodology is applied across 10 District Metering Areas (DMAs) in the north-east of Italy, each characterized by unique demographic features. Accordingly, tailored features are carefully selected for inclusion in the water demand forecast for each DMA. Full article