Urban Water Distribution Systems Research Articles

Pressure prediction of water supply network based on 1DCNN-GRU-multi-head attention

ABSTRACT The water supply network is a crucial component of urban construction and plays a pivotal role in the normal operation of the city. The complexity and periodicity of pressure variations in the water supply networks pose significant challenges to traditional prediction models. In this study, we introduce a novel short-term pressure prediction model, termed the 1DCNN-GRU-multi-head attention (CGMA) model, which incorporates a multi-head attention mechanism alongside an integrated network consisting of one-dimensional convolutional neural networks (1DCNNs) and gated recurrent units (GRUs). Initially, the model employs a 1DCNN network to extract features from the pressure data. Subsequently, the extracted features are input into the GRU neural network, leveraging its long-term dependency capabilities to improve prediction accuracy. Then, the attention mechanism is incorporated into the GRU component to highlight key information, enabling the model to focus on more important data features, thereby improving the prediction performance. We implemented this model in a real urban water distribution system equipped with five pressure sensors. The model achieved a mean absolute error of 0.00197 and a root mean square error of 0.00262. When compared with alternative approaches, our method demonstrated superior predictive performance for pressure data, thereby confirming its efficacy in practical applications.

Modeling a game to shift peak water demands: WaterTime leaderboard

Residential water demands vary with a diurnal pattern, and peak hour demands lead to inefficiencies in the operation and management of urban water distribution systems. Peak hourly demands generate significant costs due to the energy requirements of producing and pumping large volumes of potable water. High peak demands also require large investments in infrastructure expansion to support urban growth and economic development. This research develops an agent-based model to assess a demand-side management strategy that shifts high hourly demands. This research envisions a gamification system that rewards players through a leaderboard for shifting water end uses for laundry, irrigation, and bathing from on-peak to off-peak periods. Gamification of demand shifting behaviors is enabled through advanced metering infrastructure, which measures hourly or sub-hourly demands at the account level, and can be analyzed to assess water use behaviors. This research develops an Agent-based Modeling approach based on the Opinion Dynamics theory to calculate the changing opinion of household agents toward shifting water demands, based on utility broadcasts, observations of neighbor water use, social media, and participation in a leaderboard. The model is applied for eight months of hourly demand data collected through smart meters at a participating utility. Results show that a high level of participation in the leaderboard increases the adoption rate of customers shifting the time of specific end uses and may account for up to a 37 % reduction of the peak volume. The diurnal curve is flattened as consumers shift water use from on-peak to off-peak hours. The Agent-Based Model - Opinion Dynamics approach can assist water utilities in designing and evaluating gamification approaches that mitigate peak demands to support water infrastructure management.

Optimizing pump operations in water distribution networks: Balancing energy efficiency, water quality and operational constraints

Optimizing pump operation in urban water distribution systems represents a critical task for enhancing energy efficiency. Despite extensive research, achieving optimal pump operation remains a challenge due to the presence of conflicting objectives, such as minimizing energy costs and ensuring adequate pressure head availability. The recent emphasis on maintaining distributed water quality further increases the complexity of the problem. Mathematically, the optimization problem manifests as a non-convex and non-continuous search space, necessitating the application of non-derivative optimization techniques, which are highly susceptible to constraints. This work presents a comprehensive analysis of optimal pump operation, considering energy efficiency and water quality as primary objectives. A series of constraints are progressively incorporated into the model to evaluate their impact on the final operational strategy. Initially, single-objective optimization is employed using the Particle Swarm Optimization (PSO) algorithm, a robust and widely adopted approach in water resources research. Subsequently, a multi-objective algorithm, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is utilized to identify the Pareto front, encompassing both energy consumption and water quality indicators. The results demonstrate that constraints exert a significant influence on the final energy consumption and the convergence of the optimization process. Moreover, by simultaneously considering both objectives, this study proves the feasibility of identifying an optimal operational scheme that minimizes energy consumption while maintaining acceptable water quality.

Assessment of non-revenue water in the urban water distribution system network in Cameroon (Central Africa)

ABSTRACT This paper aims to assess non-revenue water (NRW) in the urban water distribution system in Cameroon. The methodological approach used in this study was based on online searches using the databases such as Google Scholar, Web of Science, PubMed, and the International Water Association. These online searches have been completed through field observations and interviews with institutional and non-institutional stakeholders in the urban water sector. Results indicate that the total volume of NRW in the urban area of Cameroon was 100.26 million m3/year, which represents 52.5% of the total system input volume. About 2 million people could be supplied by cutting Cameroon's NRW to half the present level. Findings also suggest that the water utility (Camwater) practices passive leakage control, meaning that Camwater repairs only those leaks that are visible. Reducing NRW can be achieved through leakage detection and pipe replacement, pressure management, water metering, and a program to identify, remove, and replace illegal connections.

City-scale monitoring of antibiotic resistance genes by digital PCR and metagenomics

BackgroundAnthropogenic activities significantly contribute to the dissemination of antibiotic resistance genes (ARGs), posing a substantial threat to humankind. The development of methods that allow robust ARG surveillance is a long-standing challenge. Here, we use city-scale monitoring of ARGs by using two of the most promising cutting-edge technologies, digital PCR (dPCR) and metagenomics.MethodsARG hot-spots were sampled from the urban water and wastewater distribution systems. Metagenomics was used to provide a broad view of ARG relative abundance and richness in the prokaryotic and viral fractions. From the city-core ARGs in all samples, the worldwide dispersed sul2 and tetW conferring resistance to sulfonamide and tetracycline, respectively, were monitored by dPCR and metagenomics.ResultsThe largest relative overall ARG abundance and richness were detected in the hospital wastewater and the WWTP inlet (up to ≈6,000 ARGs/Gb metagenome) with a large fraction of unclassified resistant bacteria. The abundance of ARGs in DNA and RNA contigs classified as viruses was notably lower, demonstrating a reduction of up to three orders of magnitude compared to contigs associated to prokaryotes. By metagenomics and dPCR, a similar abundance tendency of sul2 and tetW was obtained, with higher abundances in hospital wastewater and WWTP input (≈125–225 ARGs/Gb metagenome). dPCR absolute abundances were between 6,000 and 18,600 copies per ng of sewage DNA (≈105–7 copies/mL) and 6.8 copies/mL in seawater near the WWTP discharging point.ConclusionsdPCR was more sensitive and accurate, while metagenomics provided broader coverage of ARG detection. While desirable, a reliable correlation of dPCR absolute abundance units into metagenomic relative abundance units was not obtained here (r2 < 0.4) suggesting methodological factors that introduce variability. Evolutionary pressure does not significantly select the targeted ARGs in natural aquatic environments.

Social distancing, water demand changes, and quality of drinking water during the COVID-19 pandemic

The COVID-19 pandemic changed daily routines for people around the globe due to the adoption of social distancing measures, such as working from home and restricted travel. Changes in daily routines created new water demand patterns, and the spatial redistribution of water demands in urban water distribution systems affected water quality. A range of factors can influence individual decisions to social distance, including demographics, risk perceptions, and prior experience with infectious disease. This research develops an agent-based modeling framework to simulate decisions to social distance, the effect of social distancing on water demands, and effects on the performance of water infrastructure and the quality of delivered drinking water. This framework couples a hydraulic model, a COVID-19 transmission model, and Bayesian belief network (BBN) driven decision-making models within an agent-based modeling framework. The model is applied for a virtual city, Micropolis, to explore the effects of social distancing decisions on water age. Results demonstrate an increase in average water age and changes to the expected flow directions in pipes under scenarios of increasing social distancing. Nodes near industrial areas experience higher degradation of water quality. This research provides a new framework to develop and evaluate water infrastructure management strategies during pandemics.

Cluster Analysis and Predictive Modeling of Urban Water Distribution System Leaks with Socioeconomic and Engineering Factors

Cluster Analysis and Predictive Modeling of Urban Water Distribution System Leaks with Socioeconomic and Engineering Factors

Assessment of the Water Distribution Networks in the Kingdom of Saudi Arabia: A Mathematical Model

Graph theory is a branch of mathematics that is crucial to modelling applicable systems and networks using matrix representations. In this article, a novel graph-theoretic model was used to assess an urban water distribution system (WDS) in Saudi Arabia. This graph model is based on representing its elements through nodes and links using a weighted adjacency matrix. The nodes represent the points where there can be a water input or output (sources, treatment plants, tanks, reservoirs, consumers, connections), and links represent the edges of the graph that carry water from one node to another (pipes, pumps, valves). Four WDS benchmarks, pumps, tanks, reservoirs, and external sources were used to validate the framework at first. This validation showed that the worst-case scenarios for vulnerability were provided by the fault sequence iterating the calculation of the centrality measurements. The vulnerability framework’s application to the Saudi Arabian WDS enabled the identification of the system’s most vulnerable junctions and zones. As anticipated, the regions with the fewest reservoirs were most at risk from unmet demand, indicating that this system is vulnerable to the removal of junctions and pipes that are intricately associated with their neighbours. Different centrality metrics were computed, from which the betweenness centrality offered the worst vulnerability prediction measures. The aspects and zones of the WDS that can more significantly impact the water supply in the event of a failure were identified by the vulnerability framework utilising attack tactics.

Water Flow Modeling and Forecast in a Water Branch of Mexico City through ARIMA and Transfer Function Models for Anomaly Detection

Early identification of anomalies (such as leakages or sensor failures) in urban water distribution systems is critical to mitigating water scarcity in cities and is a challenge in water resource management. Several data-driven methods based on machine learning algorithms have been proposed in the literature for leakage detection in urban water distribution systems. Still, most of them are challenging to implement due to their complexity and requirements of vast amounts of reliable data for proper model generation. In addition, the required infrastructure and instrumentation to collect the data needed to train the models could be unaffordable. This paper presents the use and comparison of Autoregressive Integrated Moving Average models and Transfer Function models generated via the Box–Jenkins approach to modeling the water flow in water distribution systems for anomaly detection. The models were fit using water flow data from tanks operating in a branch of the water distribution system of Mexico City. The results showed that both methods helped select the best model type for each variable in the analyzed water branch, with Seasonal ARIMA models achieving a lower mean absolute percentage error than the fitted Transfer Function models. Furthermore, this methodology can be adjusted to different time windows to generate alerts at different rates and does not require a large sample size. The generated anomaly detection models could improve the efficiency of the water distribution system by detecting anomalies such as wrong measurements and water leakages.

Study on Gamma selection in the optimal operation of secondary water supply system based on deep Q-learning network

Abstract When the requirements for water pressure and quantity of drinking water for residents and industrial buildings exceed the capacity of the urban water distribution system, a secondary water supply system (SWSS) is supplied to users by pipelines through storage, pressurization, and other facilities. In China, SWSS has been installed in 97% of residential buildings and the operation of SWSS is directly related to the water pressure and water quality of the users' tap water. In this paper, the operation optimization objectives for the SWSS with storage facilities were proposed, and deep Q-learning network (DQN) was applied to optimize the control of SWSS. In this study, the pressure, the water age in the roof water tank, and the power consumption of the pumps were selected as the optimization objectives. The equation for the qualitative selection of the key hyperparameter (Gamma) was proposed and verified by the experiments in a community of City S in East China. The results indicated that with the decrease in the volume of the water tank, the larger Gamma value was recommended, and the more future conditions were considered. It is hoped that the result can be used as a reference in SWSS operation optimization.

Water challenges of Dire Dawa city of Ethiopia: An assessment of urban growth and groundwater supply

Urbanization in Ethiopia is increasing rapidly, leading to environmental changes and necessitating improved urban infrastructure. Dire Dawa, the second most populous city in Ethiopia, has a high rate of urban growth. This study focuses on three main areas: remote sensing analysis of urban growth, urban water supply and water distribution system, and groundwater resources management. Land use and land cover (LULC) analysis over four different time periods showed significant and rapid expansion of the city. The current and future water demand and supply analysis found that the water supply is three times less than the water demand, indicating that existing water sources are not adequate for the future expansion of the city. Moreover, the hydraulic performance of the existing distribution system as analyzed by WaterGems software found that pressure at 22.41% of nodes and velocities at 29.83% of pipes are sufficient to provide water for the study area. Finally, the study investigated the discharge and water level and water quality of each borehole in Dire Dawa City. Overall, this study showed that high urbanization rate together with the population growth has put pressure on the existing water supply source and existing water distribution system calling for feasible water management strategy for the city.

Fluorescence excitation-emission matrix spectroscopy coupled with parallel factor analysis to determine chlorine decay constants in urban water distribution system

This study applied a method for estimating chlorine decay constant (k) in urban water distribution systems using fluorescence excitation–emission matrix spectroscopy-parallel factor analysis (FEEM-PARAFAC), considering that it accounts for the influence of organic matter in the target area. The simultaneous impacts of seasonal variations on chlorine consumption and dissolved organic matter (DOM) composition were investigated for a year in three full-scale water distribution systems in I city (areas S, K, and G). Bulk decay constants (kb) were obtained through bottle tests, and the kb value was observed to differ by season and significantly affected by temperature. It exhibited its highest value, 0.794 d−1, in summer at area G. As a result of analyses through F-EEM-PARAFAC, it was determined that the components of the target raw water were humic-like and tryptophan-like. The quantitative analysis of organic substances through PARAFAC revealed that area G had the highest score (C1+C2) than other areas. 11.568, 10.578, and 11.771 in summer at areas S, K, and G, respectively. The model equations were derived such that the significant (R2 = 0.85–0.95) correlation between the C1 and C2 model scores and total chlorine decay constants (kt) verified via PARAFAC analysis of the target raw water was considered. Furthermore, a method for obtaining the wall decay constants at a target point based on the correlation equation was investigated.

CFD Investigations of Water Supply and Distribution Systems of Ancient Old and New World Archaeological Sites to Recover Ancient Water Engineering Technologies

New to archaeological studies is the field of paleohydrology characterized by the use of modern hydraulic engineering and computational fluid dynamics (CFD) analysis of ancient urban and agricultural water supply and distribution systems. Examples are presented in nine chapters of CFD investigations of old and new world archaeological sites (several of which are World Heritage sites) using FLOW-3D CFD software to bring forward new discoveries revealing the depth of ancient water engineers’ knowledge and creativity not previously noted in the archaeological literature. As modern analysis methods reveal technical details of ancient water systems, equivalent ancient technologies exist that were used in the design and operation of ancient water systems, albeit in formats, texts, and origins yet to be discovered. The nine chapters to follow present brief summaries of ancient sites’ water systems and the use of CFD and modern hydraulic engineering methods to discover the water engineering knowledge base used by ancient water engineers. Results are new revelations unknown in the current literature of ancient sites. Paleohydrology studies presented serve to add a further dimension to the history of ancient new and old world archaeological sites by bringing forward added details of water engineering projects accomplished by ancient engineers.

Optimum Configuration of Rainwater Harvesting System using CREO PARAMETRIC and ANSYS

The practice of rainwater harvesting (RWH) has been studied extensively in recent years, as it has the potential to alleviate the increasing stress on urban water distribution systems. An appropriate design and evaluation of an RWH system is necessary to improve system performance and the stability of the water supply. In this study, the design of the main components in the RWH system such as the gutter, water storage tank and water pump were varied based on the main material of the component and rainfall intensity. Each component was presented according to the results of design, stress and fluid analyses performed by Creo parametric 7.0 and ANSYS. The main outcome of this work is a computational design tool that can be used to improve the RWH system component’s performance considering a variety of factors. A design of a gutter made of polyvinyl chloride (PVC) with a yield strength of 50 MPa and a size of 5 inches was used. The rainwater will pass through a water pipe to a 500 L, High-Density Polyethylene (HDPE) water storage tank with a yield strength of 43 MPa. A 1 HP single-phase pump with a maximum flow rate of 196.2 L/min was used to generate a steady flow for household usage. The factor of safety of each component was obtained above 1.5 and was determined by the maximum Von Mises stress. Each component in the system has a different life span, however, the overall life span of the RWH system is estimated to be 20 years

Analysis of Optimal Sensor Placement in Looped Water Distribution Networks Using Different Water Quality Models

Urban looped water distribution systems are highly vulnerable to water quality issues. They could be subject to contamination events (accidental or deliberate), compromising the water quality inside them and causing damage to the users’ health. An efficient monitoring system must be developed to prevent this, supported by a suitable model for assessing water quality. Currently, several studies use advective–reactive models to analyse water quality, neglecting diffusive transport, which is claimed to be irrelevant in turbulent flows. Although this may be true in simple systems, such as linear transport pipes, the presence of laminar flows in looped systems may be significant, especially at night and in the peripheral parts of the network. In this paper, a numerical optimisation approach has been compared with the results of an experimental campaign using three different numerical models as inputs (EPANET advective model, the AZRED model in which diffusion–dispersion equations have been implemented, and a new diffusive–dispersive model in dynamic conditions using the random walk method, EPANET-DD). The optimisation problem was formulated using the Monte Carlo method. The results demonstrated a significant difference in sensor placement based on the numerical model.

Algorithm for Appropriate Design of Hydroelectric Turbines as Replacements for Pressure Reduction Valves in Water Distribution Systems

Gravity flow water distribution systems (WDSs) are used to permit water flow from an input point of highest elevation (source) to the terminal points of a system (consumers). In such WDSs, typically, there is no need for external power to maintain the flow due to the typical high gradients that exist. However, those gradients cause high water velocity and pressure to some network areas that could potentially lead to pipes bursting. Currently, the only means to regulate pressure within allowable range are Pressure Reduction Valves (PRVs). They are installed at various locations, but they cannot utilize the existing hydropower potential in terms of electrical energy production. This paper provides a methodology for hydro-turbines dimensioning, so that they mimic PRV operation in terms of pressure regulation while producing power efficiently. This includes an algorithmic process within which the proper turbine design and performance prediction are initially determined, and then, via an interconnection between EPANET and MATLAB, the hydraulic behavior of their operation within the water network is modeled and simulated. The methodology has been tested with simulations of a typical urban WDS. The results indicate that the produced turbine geometries mimic PRV action at the respective locations with more than 1% accuracy during 70% of the time (2% maximum error), while producing electric energy with hydraulic efficiency over 60%.

Modeling and optimization of pressure and water age for evaluation of urban water distribution systems performance

Water loss has become increasingly critical as the severity of the water shortage situation has grown in recent decades. One of the options for reducing water loss in urban water distribution networks is pressure management. The study aimed to evaluate and optimize the existing water distribution system in the city. The proposed methodology is an interactive combination process between an optimization algorithm and WaterGEMS V8i to evaluate the performance of the distribution system. It was observed that, 43.80% of nodes (15–60 mH2O), 5.10% of nodes (15 mH2O), and 51.10% of nodes (>60 mH2O) received pressure during peak hour demand. During low demand periods, only 4.4% of nodes (15–60 mH2O) and 95.60% of nodes (>60 mH2O) received pressure. The water age simulation results revealed that, 51.70% of the pipes were received water age <4.8 h, whereas the other 48.3% of the pipes were received water age <8.6 h during peak hour demand. During low demand periods, 45.58% of the pipes had a water age of less than 4.8 h while the other 54.42% of the pipes had water age of 4.8–20 h. The optimization result showed that after optimization, 4.4% of the nodes with optimum pressure increased to 75.18%, and 95.6% of the nodes decreased to 24.82%. Changing the size of the pipe based on the optimization result, and dividing an area into different pressure zones (adding more reservoirs at the far end of the distribution system) are all ways to improve or upgrade the distribution system.

Impact of different source-water switching patterns on the stability of drinking water in an estuarine urban water distribution system.

Source-water switching can lead to instability in drinking water distribution systems. In estuarine cities using surface water as source water where salt tide occasionally happens, the influence can be particularly complex due to changes of Larson Index (LI). The objective of this study was to investigate the effects of different switching patterns on the stability of water in an estuarine city. Fluctuated LI was found in the current distribution system. LI of the new source water was lower and more stable. Susceptible areas with a high frequency of over standard water quality were identified and pipe scales there were mainly composed of relatively stable iron oxides with dense crystal structures (Fe3O4 and α-FeOOH). Two old pipe sections were used to simulate different switching patterns. The microbial risk did not increase significantly when the original and new water sources were combined in different ratios (2∶8, 5∶5), when multiple water sources were used (3∶3∶4) or when salinity increased. The better water quality, lower LI of the new source water, and stability of the current distribution system together contributed to the biostability. Total iron increased after switching, then declined and stabilized for most switching patterns. Salt tide can lead to sharp iron release. The results provided insightful information for distribution systems that have cast iron pipes and that might encounter source-water switching patterns.

Optimizing the Control of Decentralized Rainwater Harvesting Systems for Reducing Urban Drainage Flows

The practice of rainwater harvesting (RWH) has been studied extensively in recent years, as it has the potential to alleviate some of the increasing stress on urban water distribution systems and drainage networks. Within the field, an approach of real-time control of rainwater storage is emerging as a method to improve the ability of RWH systems to reduce runoff and urban drainage flows. As applying real-time control on RWH tanks means releasing water that could be used for supply, applying controlled-release policies often hinders the RWH system’s ability to supply water. The suggested study presents an approach that has the potential to improve the capability of a distributed network of RWH systems to mitigate peak drainage flows while substantially reducing the impact on harvested rainwater availability. The suggested method uses a genetic algorithm to generate release policies, which are tailored for any given rain event and initial conditions. The algorithm utilizes the modeled drainage system’s response to a given rainfall pattern and manages to substantially reduce peak drainage flows with little impact on available rainwater when compared to the conventional no-release alternative and other active release methods.

Modeling and Optimization of Pressure and Water Age for Evaluation of Urban Water Distribution Systems Performance

Modeling and Optimization of Pressure and Water Age for Evaluation of Urban Water Distribution Systems Performance