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Drinking water distribution systems must safely meet the expected demand for water. However, sudden pipe breaks can limit the continuous supply of drinking water. As operating pressure is among the factors affecting the probability of pipe breaks, a methodology was developed in this study to identify the pressure covariates that affect the frequency of water distribution pipe breaks. Five pressure covariates obtained from hydraulic simulations based on measured pressure and flow rate time series were evaluated using a likelihood ratio test to compare the maximum likelihood function values of a pipe break model calibrated with no covariates with those of the same model calibrated with a single pressure covariate. This pipe break model was calibrated using the recorded history of pipe breaks over a six-year period and applied to two district metered areas in the water distribution network of Quebec, Canada. The results indicated that the maximum and mean pressure covariates were significantly associated with the occurrence of pipe breaks. [ABSTRACT FROM AUTHOR]

Water Scarcity is a very severe problem across the world, one of the main factors is improper distribution of water and careless use of water by people, this is only going to be more severe in future as population and needs of the world rises. Many countries have increased deployment of smart water meters to monitor water usage and tried convincing people to not use water in a careless manner but have not been successful yet. This research paper presents the development and implementation of a smart water meter (SWM) prototype for household water consumption measurement. The SWM utilizes Wi-Fi or Long Range (LoRa) technology to transmit data and is integrated with Citizen Id (SSN) to centralize water distribution, and help detect water theft. Additionally, the meter incorporates SARIMA forecasting to predict water consumption based on past usage trends on the edge. The water consumption data can be accessed through a web and Android application, and an integrated billing system has been developed to provide users with information about their current water usage. The machine learning model was trained and tested on the water consumption dataset by DAIAD. The DAIAD dataset consists of hourly water consumption time series for 1,007 randomly selected consumers from the AMAEM (Association of Energy and Water Management) utility in Alicante, Spain, spanning from January 2015 to May 2017, totaling 16,857,056 measurements. The whole system was tested by installing it in a house and the forecasting model achieved an accuracy of 74%. [ABSTRACT FROM AUTHOR]

Open water transposition channels in hot and arid regions, like those in the São Francisco River Integration Project (PISF) in Brazil, suffer significant water losses through evaporation. This paper proposes covering these channels with photovoltaic (PV) panels to reduce evaporation while simultaneously generating clean energy. The research aims to quantify water savings and energy generation potential across all channel lengths and assess whether the generated solar power can substitute grid electricity for powering the transposition pumps during peak hours, thereby enhancing energy efficiency. This study analyzed the state-of-the-art of PV generation and calculated their solar potential. Identified the specific characteristics of PISF channels and watercourses considering the regional geography, meteorology, irradiation, and social peculiarities. And, finally, assessed the feasibility of covering the watercourses with solar panels. The results reveal that covering all current PISF channels with PV panels could save up to 25,000 cubic meters of water per day, significantly contributing to water security and improving the quality of life for the local population. Additionally, the project could generate 1200 gigawatt-hours of electricity annually, meeting the energy demands of the transposition pumps during peak hours and promoting energy efficiency within the project. This research paves the way for utilizing PV technology to address water scarcity challenges and enhance the sustainability of water infrastructure projects in arid regions worldwide. [ABSTRACT FROM AUTHOR]

"The Book of Secrets in the Results of Ideas", usually called "The Book of Secrets" is a codex containing drawings and descriptions of thirty-one artifacts attributed to the engineer Alī Ibn Khalaf al-Murādī, who lived in Andalusia in southern Spain at the beginning of the 11th century. This manuscript is one of the first written testimonies that describe medieval mechanisms with complex precision. The aim of this work is to reconstruct and study from a historical and technological point of view two of the ancient artifacts contained in the "Book of Secrets", the "Fortress Demolisher" and the "Magic Well". The "Fortress Demolisher" is a war machine designed to demolish the upper part of the walls or towers and consists of a battering ram mounted on a platform that can be raised several meters above the ground using a scissor mechanism. The "Magic Well" is a mechanism with several pulleys whose purpose is to raise water several meters from the bottom of a well by collecting a small portion of rope. To reconstruct the two artifacts, a digital model and a detailed study of the geometry and operation of the mechanisms, were first carried out. 3D printing, using the Fused Deposition Modelling technology, made the physical reproduction. The digitization and physical reproduction by 3D printing of the mechanisms represent an update of the traditional ways of transmitting knowledge. The use of these new technologies makes the knowledge more accessible, understandable and attractive. [ABSTRACT FROM AUTHOR]

The use of technology in drinking water treatment plants can be employed to reduce the uncertainty between urban water supply and demand. This study introduces the application of effluent treatment in drinking water treatment plants to reduce the generated effluent and balance groundwater resources. To achieve this, poly-electrolyte compounds were employed to treat the generated effluent in drinking water treatment plants, resulting in significant sedimentation of the Effluent volume and increased availability of water to meet the demands. In the drinking water treatment plants of Tehran city, to compensate for aquifer deficits, 50 million cubic meters of water per year were discharged as effluent from the cycle. However, by modifying the recycling process and utilizing poly-electrolyte, it is possible to reintegrate this water into the cycle. This water volume can be utilized as a substitute for exploiting groundwater resources. To assess the effectiveness and prioritize water allocation, three technical, economic, and social criteria were defined and calculated for each water treatment plant. To evaluate the impact of this process change on the groundwater level, a hybrid model called ANN–WOA was utilized. Based on this, four variables, including groundwater level, recharge, precipitation, and groundwater discharge, were defined as input variables to the model. 10 years was used for training, and 2 years was used for testing the model. Ultimately, the quantitative improvement of the groundwater was assessed the downstream of each water treatment plant using an appropriate index of desirability. The economic index value for allocation and the level of social satisfaction based on stakeholders' perspectives were completed. Ultimately, in the SWARA–COPRAS multi-criteria decision-making model, the prioritization of allocation for each water treatment plant was determined. The results demonstrated that by substituting this water volume, the desirability index increased by an average of 23%, which can be effective in balancing groundwater resources. Furthermore, the results indicated that the priority for allocation was given to the industrial sector, followed by the nearest green spaces available. The findings of this study illustrate that the use of new technologies in effluent treatment plants can be effective in reducing the exploitation of groundwater resources and achieving groundwater balance. [ABSTRACT FROM AUTHOR]

Developments in data mining techniques have significantly influenced the progress of Intelligent Water Systems (IWSs). Learning about the hydraulic conditions enables the development of increasingly reliable predictive models of water consumption. The non-stationary, non-linear, and inherent stochasticity of water consumption data at the level of a single water meter means that the characteristics of its determinism remain impossible to observe and their burden of randomness creates interpretive difficulties. A deterministic model of water consumption was developed based on data from high temporal resolution water meters. Seven machine learning algorithms were used and compared to build predictive models. In addition, an attempt was made to estimate how many water meters data are needed for the model to bear the hallmarks of determinism. The most accurate model was obtained using Support Vector Regression (8.9%) and the determinism of the model was achieved using time series from eleven water meters of multi-family buildings. [ABSTRACT FROM AUTHOR]

The focus of this study is on large flow meters, for which there is limited information due to their large size, making testing and research challenging. This research was conducted to quantify the effects of various accessories located upstream of these meters. Even a small percentage of variation in error can correspond to a significant volume of water. Accurate meters are crucial in many applications that involve large volumes of water as precise flow measurements are necessary for smooth and efficient processes while avoiding costly errors and downtime. Inaccurate large water meters can have far-reaching implications, such as overbilling or underbilling and production inefficiencies, which result in wasted resources and energy. Furthermore, inaccurate flow measurements can lead to environmental consequences as industries must comply with strict regulations regarding wastewater discharge limits. Uncertainty about the economic impact of an accessory installed upstream of a medium-sized water meter leads many water utilities to oversize the meter chambers to mitigate potential negative errors. In this study, six types of elements were tested upstream of ten brand-new water meters from six different manufacturers, constructed using four different metering technologies: single-jet, Woltmann, electromagnetic, and ultrasonic. Each meter unit was tested at five flow rates, ranging from the minimum to overload. The tests were conducted with accessories set in different orientations and distances upstream of the water meters under study. The research shows that the accessories used can cause significant deviations in measuring errors compared to the regular errors found under undisturbed working conditions. [ABSTRACT FROM AUTHOR]

The interruption of supply in water distribution systems is a common management practice particularly prevalent in developing countries. Upon reopening, the air is expelled through user service pipes, which causes over-reading and seriously impacts the water meter reliability. This paper aims to assess by laboratory tests the effects of air flow on water meter performance by laboratory tests, using a set-up with diameters comparable to those of real water systems. The improvements introduced by applying unmeasured-flow reducer and air valves at different locations are also tested. The relevance of the results of the laboratory activity for practical applications is discussed, both for reducing the over-reading and improving the reliability of the water meters. [ABSTRACT FROM AUTHOR]

This study presents a planning model to assist decision-makers in implementing proposed sustainable development policies based on the government's development schemes. It focuses on the exploitation of groundwater from the Eocene aquifer in the West–West Minya region in the Western Desert of Egypt as one of the new development areas. The visual MODFLOW model is applied to serve as a base model for the study area's local modeling and assess the impact of operating scenarios on the groundwater aquifer. An optimal scenario for groundwater sustainability is achieved by considering the water meter consumption in the newly reclaimed area with a value of 6 m3/d for each acre to irrigate 3953.68 acres; the maximum drawdown of about 60 m is formed after 9700 days of simulation. The GIS multi-criteria analysis model is used to assess the impact of groundwater deficiency as a result of reclamation. Results of the groundwater model are merged as input layers in multi-criteria decision analysis, which include groundwater salinity, groundwater levels, aquifer transmissivity, and aquifer storativity. The prospective groundwater zones are categorized into most suitable (1584 km2, 9.9%), suitable (4592 km2, 28.7%), good (4784 km2, 29.9%), moderate (1488 km2, 9.3%), and unsuitable (3552 km2, 22.2%). It is recommended to optimize levels of groundwater withdrawal from the current or future drilling wells to achieve a balance between the use and the protection of water potential. [ABSTRACT FROM AUTHOR]

Water quality monitoring allows communities to achieve sustainable management of water resources, which is crucial for life-supporting processes. Water quality is determined by measuring chemical, physical, and biological parameters, requiring sophisticated meters and trained specialists to perform the measurement. However, in low-income communities, water quality is determined by using human senses—smell, color, and taste—since meter acquisition is limited by costs and most people do not know how to monitor water quality. Therefore, accessible technology is necessary to empower communities to have a sustainable lifestyle. In this paper, we present the design and implementation of PortAqua, a 2-parameter water quality meter (WQM), to promote training on water quality measurement. Using basic electronic components, PortAqua is capable of measuring pH with an error of 0.4, and conductivity with an error of 33% at 85 µS cm-1, and 8.7% at 1413 µS cm-1. To demonstrate its preliminary effectiveness as a WQM and its science communication capabilities, the meter has been used in a hands-on workshop with undergraduate and graduate students. During the workshop, attendees participated in a short lecture about water quality measurement techniques and local regulations. Then, they collected water samples from a local source, measured the samples using PortAqua, and discussed the results based on the concepts and regulations. The workshop's effectiveness was evaluated through pre- and post-assessments which revealed increased knowledge of water quality regulations, measurement, and parameters at the end of the activity. [ABSTRACT FROM AUTHOR]

Floating photovoltaic system for reservoirs is a recent innovative technology that is highly advantageous in reducing evaporation while generating solar power. In addition, the integration of floating photovoltaic systems with the existing hydroelectric power plants will increase renewable power production. The present study aims to assess the electrical performance of floating photovoltaic systems in major reservoirs with existing hydroelectric power plants in India. The reservoirs with large water surface area were selected for the study, and a model floating photovoltaic system with a 5-MW capacity was designed for the selected reservoirs. The numerical analysis showed that installing floating photovoltaic systems will result in an annual energy yield of 160 GWh. Further, the systems also save 1.40 million cubic meters of water per day and also help in generating additional energy of 514.80 MWh/day from the saved water through its integration with hydroelectric power plants. A single-axis tracking mechanism to the floating photovoltaic systems will increase the annual energy generation by 11%. The detailed cost analysis and carbon emission analysis were also carried out. The results indicate that the tracking mechanisms increase the total installation cost of the systems. The annual carbon emission reduction from the floating photovoltaic systems accounts for about 3.30 million tons of CO2. The obtained results highlight the suitability of this innovative technology for installation in Indian reservoirs and its effectiveness in reducing evaporation and carbon emission. [ABSTRACT FROM AUTHOR]

At present, there are still many old-fashioned water meters in the society, and the water department needs to send staff to read the water meter after arriving at the scene with a handheld all-in-one machine. However, there are many problems in this manual meter reading method. First, a large number of meter reading work leads to low efficiency of the entire water department, consuming a lot of time and energy, and high labor costs; second, the water meters in natural scenes have problems such as serious dial contamination and other environmental factors that interfere with the meter reading staff, and the results of the meter reader cannot be verified later. In response to these problems, this paper studies a deep learning method for automatic detection and recognition of water meter readings. This paper first introduces the existing in-depth learning models, such as Faster R-CNN, SSD, and YOLOv3. Then two datasets are sorted out, one is the original water table picture dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Then two plans are proposed, one is the original water table image dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Finally, by comparing the three models from different angles, it is determined that YOLOv3 in the second solution has the best recognition effect, and the accuracy rate reaches 90.61%, which can greatly improve work efficiency, save labor costs, and assist auditors in reviewing the read water meter readings. [ABSTRACT FROM AUTHOR]

There are over 52,000 dams in the contiguous US ranging from 0.5 to 243 meters high that collectively hold 600,000 million cubic meters of water. These structures have dramatically affected the river dynamics of every major watershed in the country. While there are national datasets that document dam attributes, there is no national dataset of reservoir operations. Here we present a dataset of historical reservoir inflows, outflows and changes in storage for 679 major reservoirs across the US, called ResOpsUS. All of the data are provided at a daily temporal resolution. Temporal coverage varies by reservoir depending on construction date and digital data availability. Overall, the data spans from 1930 to 2020, although the best coverage is for the most recent years, particularly 1980 to 2020. The reservoirs included in our dataset cover more than half of the total storage of large reservoirs in the US (defined as reservoirs with storage greater 0.1 km3). We document the assembly process of this dataset as well as its contents. Historical operations are also compared to static reservoir attribute datasets for validation. Measurement(s) storage • inflow • outflow • evaporation • elevation Technology Type(s) digital curation Factor Type(s) temporal interval Sample Characteristic - Environment reservoir Sample Characteristic - Location contiguous United States of America Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.17161415 [ABSTRACT FROM AUTHOR]

Microplastics (MP) as emerging persistent pollutants were found in raw and drinking water worldwide. Since different methods were used, there is an urgent need for harmonized protocols for sampling, sample preparation, and analysis. In this study, a holistic and validated analytical workflow for MP analysis in aqueous matrices down to 5 μm is presented. For sampling of several cubic meters of water, an easily portable filter cascade unit with different pore sizes (100–20–5 μm) was developed and successfully applied for the sampling of three processed drinking waters, two tap waters and one groundwater. The size distribution and polymer types of MP were determined using a two-step semi-automated Raman microspectroscopy analysis. For quality control, comprehensive process blanks were considered at all times and a recovery test yielded an overall recovery of 81%. The average concentration of identified MP was 66 ± 76 MP/m3 ranging from 1 MP/m3 to 197 MP/m3. All found concentrations were below the limit of quantitation (LOQ) of 1880 MP/m3. The majority consisted of PE (86% ± 111%) while comparatively low numbers of PET (10% ± 25%), PP (3% ± 6%), and PA (1% ± 4%) were found. 79% of all particles were smaller than 20 μm. In summary, this study presents the application of a workflow for sampling and analysis of MP down to 5 μm with first results of no significant contamination in drinking water and groundwater. [ABSTRACT FROM AUTHOR]

Saudi Arabia tries to build local desalination water stations to supply water to remote areas. Due to the low cost and energy requirements of reverse osmosis (RO) desalination technology, it has been used to supply fresh water to Arar City in the northeast of Saudi Arabia. In this paper, it is proposed to provide an average of 1000 cubic meters of water per day by using autonomous hybrid renewable energy system (RES). This proposed system contains wind turbines (WTs), photovoltaic (PV), battery, and it is designed to feed the RO system with the energy adequate to produce the required amount of fresh water for the minimum cost and minimum loss of supply probability. The proposed system was designed to generate 2440 kW power to produce this amount of water. Matching study between the site and the best WT among 10 market-available WTs is introduced. Three optimization strategies were used and compared for the design of the proposed system to ensure that no premature convergence can occur. These strategies consisted of two well-known techniques, particle swarm optimization and bat algorithm (BA), and a relatively new technique: social mimic optimization. The simulation results obtained from the proposed system showed the superiority of using a RES for feeding a RO desalination power plant in Arar City, and they also showed that the BA is the fastest and most accurate optimization technique to perform this design problem compared with the other two optimization techniques. This detailed analysis shows that the cost of production of fresh water is $0.745/m3. [ABSTRACT FROM AUTHOR]

The water sector in India has undergone several institutional and structural reforms through different policies and legislation changes over the past decades to address the water scarcity challenges. This study presents and analyses various water utility policies chronologically emphasising persuasive conservation and use efficiency approach towards their implementation. A review of existing policies indicated that most of them have been only partially effective in implementing efficient water management due to the lack of a need-specific institutional framework, further deepening the water crisis. Moreover, the Indian water sector has been identified to concentrate majorly on water resource management, while overlooking sustainable aspects like water use efficiency through water metering. This study performed an 'indicator-based analysis', applying the framework of bounded rationality coupled with a 'cause-effect' model to recognise the effect of 12 significant water supply policies executed since independence. While initial policies focused on coverage and supply of adequate, safe, and improved drinking water majorly to rural sectors, the water sector gained holistic importance during the post-1990s considering aspects of the water source, supply frequency, water tariffing, etc. However, the gap coherence analysis illustrated that the concept of 'water metering' has not been distinctly considered in these policies. The findings from this study indicate a need for a discrete water metering policy, an equitable, universal, and cost-reflective tariff structuring, and the formulation of an institutional metering framework involving codes, testing laboratories, water metering standard associations. The above offers an alternate approach to the existing policy and a feasible solution to the water crisis in India. Internalising the advantages of metering in policy agenda may unlock the possibilities of success of the forthcoming policies. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN's) are preferred for industrial applications due to progressive increase of sensor electronics. One such application is deployment of WSN's in smart grids. Smart Grid integrates information and communication techniques with electricity network. Smart grids utilize sophisticated control and monitoring devices for improving the efficiency of the grid. For energy efficient, low cost monitoring and control in smart grid WSN's is treated as a promising technology. Advanced Metering Infrastructure (AMI) is the key technology in the distribution networks of Smart Grid. The AMI is composed of various sensors for metering purpose. The meter data is also useful for the distribution operators to manage the demand response. The network involves smart meters, smart electric gas and water meters along with digital network management appliances for optimizing the electric network with real time data management. The smart sensors are limited in terms of battery, operational power and memory. These sensors communicate with the base station in restricted range. The communication between smart grid nodes and base station (sink) is multi-hop in nature. The communication takes place within limited range of communication so the security concerns that are involved in the network are to be handled by the routing protocols. So as to make the bidirectional communication efficient between the smart sensors and utility an effective routing scheme is required for these energy limited devices to handle the heavy network traffic in smart grids. Here energy efficient routing for WSN's in NAN networks to attain load balancing is proposed through density based Fuzzy C means clustering (DFCM). The obtained simulation results show that DFCM can provide a satisfactory performance for enhancing the network life span. [ABSTRACT FROM AUTHOR]

Water leakage in the urban water cycle involves relevant economic, social, and environmental negative impacts. Thus, reducing water leakage is a key challenge for both water regulators and water companies. This study estimated the evolution (2007–2015) of the marginal cost of improving the quality of service in terms of water leakage in the Chilean water industry, which involves full private, concessionary, and public water companies. In water companies, management skills and efforts play an important role in meeting water leakage targets. Thus, this study employed a cost frontier model where it was assumed that unobserved management ability interacts with output and water leakage factors. The results reveal high levels of cost efficiency for the average water company. Management increases outputs and reduces water leakage and, thus, has a positive impact on costs and efficiency. The marginal cost of reducing water leakage is higher for the public water company than for private and concessionary water companies. The average estimated marginal cost of reducing water leakage was 0.349 €/m3, which means that a water company has to spend an extra 0.349 € in operating costs to avoid a cubic meter of water leakage. Some policy implications were discussed based on the results of this study. [ABSTRACT FROM AUTHOR]

Hysteresis of soil water retention curve (SWRC hysteresis) is widely used for modeling water flux in an unsaturated soil during infiltration and irrigation processes. The aim of the work was to predict soil moisture of a wetting curve from the main drying branch based on an effective degree of saturation for the drying branch by suggesting pedotransfer functions (PTFs). Furthermore, the efficiency of the proposed PTFs (M-1) was estimated by comparing it with a model having the same purpose such as a model of Mualem (1977) (M-77). The drying and wetting branches were measured using capillary meter at soil water pressure heads lower than –1000 mbar. The effective degree of saturation for drying and wetting curves was estimated at different values of soil water pressure heads. The proposed PTFs (M-1) = was derived by dividing the mean values of to the mean values of . The results show that the estimation error for the proposed PTFs (M-1) and the Mualem model (M-77) was less for estimating soil moisture of a wetting curve at the higher values of soil water pressure heads as –500, –700 and –900 mbar than their values calculated at the lower values of soil water pressure heads as –50, –150, and –300 mbar and near of saturation. While the proposed PTFs (M-1) was more accurate than the Mualem model (M-77) for calculating soil moisture of a wetting branch at a saturation point and at the lower values of soil water pressure heads as –50, –150, and –300 mbar. The proposed PTFs (M-1) = can be used for simulating soil moisture of wetting branch during infiltration and irrigation processes at soil water pressure heads lower than ‒1000 mbar and a near of saturation for agrosoddy-podzolic soil of silt loam and silty clay loam textures. [ABSTRACT FROM AUTHOR]

Deep learning models are tools for data analysis suitable for approximating (non-linear) relationships among variables for the best prediction of an outcome. While these models can be used to answer many important questions, their utility is still harshly criticized, being extremely challenging to identify which data descriptors are the most adequate to represent a given specific phenomenon of interest. With a recent experience in the development of a deep learning model designed to detect failures in mechanical water meter devices, we have learnt that a sensible deterioration of the prediction accuracy can occur if one tries to train a deep learning model by adding specific device descriptors, based on categorical data. This can happen because of an excessive increase in the dimensions of the data, with a correspondent loss of statistical significance. After several unsuccessful experiments conducted with alternative methodologies that either permit to reduce the data space dimensionality or employ more traditional machine learning algorithms, we changed the training strategy, reconsidering that categorical data, in the light of a Pareto analysis. In essence, we used those categorical descriptors, not as an input on which to train our deep learning model, but as a tool to give a new shape to the dataset, based on the Pareto rule. With this data adjustment, we trained a more performative deep learning model able to detect defective water meter devices with a prediction accuracy in the range 87–90%, even in the presence of categorical descriptors. [ABSTRACT FROM AUTHOR]

Rainwater harvesting is adopted to face water scarcity in arid regions. Many studies were developed in Jordan to estimate the potential of water harvesting for several uses. However, the precise estimation of water saving and cost benefits for the potential of rainwater harvesting from the roofs of residential urban areas is insufficient. Therefore, the objective of this study was to investigate the feasibility of rainwater harvesting from residential rooftops in all the eighty-nine Jordanian districts. The forecast number of buildings was calculated from 2016 to 2025 using building growth rate, where the number of houses and villas was adopted only. The long-term rainfall average from 1937 to 2017 was used. Two scenarios were used to assess the potential of rainwater harvesting; numerical (as scenario 1) and tabulated by plumbing code (as scenario 2) for the years from 2019 to 2025. Also, the growth rate of one cubic meter of water cost was calculated to find the money saving potential for the water companies in Jordan. The results indicated that the water harvesting potential was different between two scenarios in the districts which have annual rainfall more than 100 mm was efficient in scenario 1 compared to scenario 2 with the projected financial return which was increased from $5.4 million at 2019 to reach $33.4 million at 2025, while in the districts which have annual rainfall less than 100 mm was more efficient in scenario 2 compared to scenario 1 with the projected financial return which was increased from $2.4 million at 2019 to reach $14.6 million at 2025. [ABSTRACT FROM AUTHOR]

The current study aims to monitor, detect, and assess post meter water leakage in public areas including schools, graveyards, public toilets, parks, and universities. Twenty-two different public sites were selected within the current study and smart water meters of various sizes were installed in these pilot study sites (PSS). A real time monitoring program was conducted to measure the flow rates at these sites at 15-min time intervals for a period of six months in 2019. The flow occurrence was monitored every two minutes within a user predefined period of inspection, and hence a leak warning message is generated once a continuous flow was detected. Minimum water usage was observed during the minimum night flow (MNF) period from 00:00 to 05:00, therefore close monitoring of the MNF at shorter time intervals allows appropriate leakage detection with proper estimation of leakage volumes. High levels of post meter leakages were observed at all the PSS where the total leakage exceeded 58% of the total flow. On-site investigations and close monitoring revealed that significant water leakage volumes in these PSS are taking place due to poor management practices where deteriorated water connections in toilets and water taps are the main contributing factors to the water leakages in most of these PSS. The results indicated that such huge leakage can be resolved at a low cost. A frequency analysis of the monitored flow rates was found efficient in analyzing the suitability of the installed meter sizes. [ABSTRACT FROM AUTHOR]

Supervised Machine Learning (ML) requires that smart algorithms scrutinize a very large number of labeled samples before they can make right predictions. And this is not always true either. In our experience, in fact, a neural network trained with a huge database comprised of over fifteen million water meter readings had essentially failed to predict when a meter would malfunction/need disassembly based on a history of water consumption measurements. With a second step, we developed a methodology, based on the enforcement of a specialized data semantics, that allowed us to extract only those samples for training that were not noised by data impurities. With this methodology, we re-trained the neural network up to a prediction accuracy of over 80%. Yet, we simultaneously realized that the new training dataset was significantly different from the initial one in statistical terms, and much smaller, as well. We had reached a sort of paradox: We had alleviated the initial problem with a better interpretable model, but we had changed the replicated form of the initial data. To reconcile that paradox, we further enhanced our data semantics with the contribution of field experts. This has finally led to the extrapolation of a training dataset truly representative of regular/defective water meters and able to describe the underlying statistical phenomenon, while still providing an excellent prediction accuracy of the resulting classifier. At the end of this path, the lesson we have learnt is that a human-in-the-loop approach may significantly help to clean and re-organize noised datasets for an empowered ML design experience. [ABSTRACT FROM AUTHOR]

As water is a scarce natural resource, one of the most crucial aspects influencing its management is the measurement of user consumptions. There are many studies which set out to analyze issues related to water meter accuracy from either a technical or economical point of view. This investigation proposes an approach that integrates both technical and economic studies to advise in the error evaluation and the units renewal decisions. The technical study includes a methodology for measuring the error produced at different flow rates and an analysis of the results obtained. In the economic study three methods – linked to different management strategies – have been outlined to make an adequate appraisal of the water meter replacement time. The procedure was applied to the water meter park of a medium-sized Spanish city. Results indicate that both measured volume and age contribute to the error evolution, and that there was no noticeable influence regarding either the transmission type or the brand. In the economic study, specific results related to the water replacement were obtained, and a sensibility analysis revealed the influence of the price of water, water meter cost and the cost of capital. [ABSTRACT FROM AUTHOR]

Recent advances in intelligent water meter technology have improved the quantitative monitoring in water supply and distribution systems. Smart meters using automated meter reading (AMR) technology allow water utilities to: (a) provide clear consumption patterns which can help customers to track and control their water usage and (b) improve active leakage targeting and leak detection capability. This paper presents a feedback about the use of AMR system to detect leakage in a large-scale experimentation, which is conducted at the Scientific Campus of the University of Lille, which stands for a small town of 25,000 users. This paper presents the demonstration site as well as its monitoring using AMR technology and how this technology allowed a rapid detection of water leakage. [ABSTRACT FROM AUTHOR]

Essential service providers face unique challenges that are specific delivering a secure and safe water and energy supply to remote communities such as islands and isolated mainland townships. Many remote communities rely on energy intensive water supply systems which are inherently costly to operate. Water demand management programmes such as retrofitting households with water-efficient devices and appliances are one way of reducing the water-energy costs in these communities. This paper presents modelling results from a comparison between business as usual and a scenario where water-efficient strategies were retrofitted in households in three remote communities in Northern Australia. The modelling demonstrated considerable savings to both the water and energy average daily consumption and associated economic costs though reduced reliance on desalination plants and bore pumping. The retrofit scenario was shown to reduce water demand by between 14 and 39 ML/y and total energy demand in off-grid communities by between 83 and 208 MWh/y. Cost reductions for delivering treated water to households ranged between around AUD$11,000 and AUD$70,000 per year, depending on uptake rates of the retrofit programme by each community. This paper forms part of Stage 1 of the Remote and Isolated Communities Essential Services (RICES) project. Stage 2 will confirm many of the assumptions underlying the modelling and build on the smart metering datasets and community engagement process currently underway in Stage 1. The overall outcome of the RICES project is to ensure that such strategies are practical to implement on a broader scale to ultimately achieve more sustainable water and energy efficient off-grid communities. [ABSTRACT FROM AUTHOR]

The aim of this project is to assign domestic water consumption to different devices based on the information provided by the water meter. We monitored a sample of Barcelona and Murcia with flow switches that recorded when a particular device was in use. In addition, the water meter readings were recorded every 5 and 1 s, respectively, in Barcelona and Murcia. The initial work used Barcelona data, and the method was later verified and adjusted with the Murcia data. The proposed method employs an algorithm that characterizes the water consumption of each device, using Barcelona to establish the initial parameters which, afterwards, provide information for adjusting the parameters of each household studied. Once the parameters have been adjusted, the algorithm assigns the consumption to each device. The efficacy of the assignation process is summarized in terms of: sensitivity and specificity. The algorithm provides a correct identification rate of between 70 % and 80 %; sometimes even higher, depending on how well the chosen parameters reflect household consumption patterns. Considering the high variability of the patterns and the fact that use is characterized by only the aggregate consumption that the water meter provides, the results are quite satisfactory. [ABSTRACT FROM AUTHOR]

We design a socially-efficient water tariff in the institutional context of England, where water metering is largely optional and non-metered households are levied proportional to the rateable value (RV) of their property. Within this context, it is theoretically demonstrated that: the larger the RV, the more likely the household to opt for metering; and the larger the RV, the smaller the Demand Effect of Metering (DEM; the fall in water consumption resulting from metering). These two hypotheses are confirmed with econometric analyses using datasets provided by a water company operating in East Anglia, England. The results signify an adverse-selection problem: wealthier households are more likely to opt for metering, yet they are expected to exhibit a smaller DEM once a meter is installed. In order to overcome this, we propose a two-part tariff for metered households consisting of: a variable charge levied proportional to water consumption at a uniform price; and a progressive standing charge to place a heavier burden on wealthier households. The latter component has a potentially major role in attaining social efficiency of metering, by encouraging poorer households to install meters whilst discouraging wealthier ones. The optimal two-part tariff is determined empirically. [ABSTRACT FROM AUTHOR]

This study presents a low-power all-digital clock generator (ADCG) for a wide supply voltage range system. The proposed ADCG limits the maximum supply current to 100 μA at a supply voltage ranging from 1.6 to 3.6 V. The ADCG also uses a digitally controlled oscillator (DCO) to extend its operational frequency range. The proposed DCO controls the supply current and divider circuits for a wide supply voltage range. The output duty cycle of ADCG falls within 50 ± 1.9 % using a duty cycle corrector. The maximum peak-to-peak jitter is less than 2.7 % at 8.38 MHz for a digital water meter application (DWM). The operational frequencies of 1.45 and 8.38 MHz at 1.8 V are 3.1 and 36.7 μA, respectively. The core area of ADCG is 0.14 mm for a 0.35 μm CMOS process. The operational frequency of ADCG ranges from 4.5 to 9.2 MHz at a supply voltage ranging from 1.6 to 3.6 V. This clock generator can also be applied to microcontroller applications. [ABSTRACT FROM AUTHOR]

Using TOPEX satellite altimetry, water-level-gauge data, and a geoid model, the geopotential, W 0, of the International Great Lakes Datum of 1985 (IGLD85) is determined. This is compared to an analogous determination using GPS and leveling data in the region. The two sets of data yield generally consistent results at the few-decimeter level and both indicate a tilt of about 33 cm in the computed datum across the region. On the basis of this and other studies, it is conjectured that the source of the tilt is a regional error in G99SSS. Further analysis of the altimetry and water-level data indicate that the geoid model, G99SSS, is in error by up to 20 cm at scales of about 100?150 km. In addition, the analysis of 8 years of altimeter and water-level data shows varying trends (up to 2 mm/yr) in crustal uplift throughout the region, generally consistent with an independent post-glacial rebound (PGR) model, ICE-4G. [ABSTRACT FROM AUTHOR]

The article presents a study which aims to monitor the performance of activated carbon filter utilized in removing ethylene dibromide (EDB) from an EDB-contaminated well water more than one-year time period. The study utilizes an installed Hersey/Amitrol American Water Meter to measure water flow in the carbon filter.

Smart water meter, which incorporates IoT (Internet of Things) technology, is receiving high attention due to recent development of information and communication technology. If traditional mechanical water meters are replaced by electronic ultrasonic water meters, micro flow rate can be measured and the measurement uncertainty can be improved due to the age of use. This enables smart metering such as AMR (Automatic Meter Reading) or AMI (Advanced Metering Infrastructure) as well as various water related services. In this paper, a low power ultrasonic water meter will be designed to operate with a battery for a long period of time. A water meter shall be designed to operate for at least 9 years, which is the requirement for type approval. In this paper, a low-power modeling is performed for battery-operated ultrasonic water meter to work for at least 10 years. The proposed low power embedded system model will be verified with actual test circuits. [ABSTRACT FROM AUTHOR]

In this paper, we describe the design of a machine learning-based classifier, tailored to predict whether a water meter will fail or need a replacement. Our initial attempt to train a recurrent deep neural network (RNN), based on the use of 15 million of readings gathered from 1 million of mechanical water meters, spread throughout Northern Italy, led to non-positive results. We learned this was due to a lack of specific attention devoted to the quality of the analyzed data. We, hence, developed a novel methodology, based on a new semantics which we enforced on the training data. This allowed us to extract only those samples which are representative of the complex phenomenon of defective water meters. Adopting such a methodology, the accuracy of our RNN exceeded the 80% threshold. We simultaneously realized that the new training dataset differed significantly, in statistical terms, from the initial dataset, leading to an apparent paradox. Thus, with our contribution, we have demonstrated how to reconcile such a paradox, showing that our classifier can help detecting defective meters, while simplifying replacement procedures. [ABSTRACT FROM AUTHOR]

Conventional water metering has a tendency of being unreliable, inaccurate, and a source of dispute and disquiet among customers, and hence it is considered to be one primary reason why water service providers perform poorly and have very low revenue collection figures. The result of this poor performance spirals into poor management, ineffective maintenance, inaccurate billing and high non-revenue water. This study explored what a prepaid metering project in Nakuru, Kenya achieved, and to see whether or not prepayment can be considered to be an innovative way of addressing these concerns, especially how to provide better service, when providing water to the urban poor. In addition, the study investigated micro financing as tools for supporting efforts by water service providers serving the poor and whether they are an effective way of financing projects. Communal prepaid meters were installed in an existing conventional network and data on commonly used performance evaluation parameters collected through interviews, field visits, and existing literature. The results portrayed prepaid metering with positive improvements to the conventional metering. It was found that there is an opportunity in microfinance for win-win partnerships among financial institutions, water service providers and low-income customers to help them in increasing access to water services. Communal prepaid meters are now bankable and viable options for serving urban poor communities. [ABSTRACT FROM AUTHOR]