Water Distribution Networks Research Articles

Generating zonal hourly water consumption patterns in water distribution networks using end-use data

ABSTRACT patterns is a requisite for the design and operation of water distribution networks. This study uses end-use water consumption data to create zonal water consumption patterns. The process involves extrapolating end-use data to create flow variation in the water distribution network. In this study, the water consumption patterns of 30 household consumers as the end-use data were monitored for a period of one year. To validate the results of the presented methodology, zonal water consumption patterns of nine district-metered areas were also measured. The Normal and the Gumbel probability distributions were applied to individual water consumption patterns to generate comprehensive zonal water consumption patterns for different population sizes. The results indicate that the maximum Gumbel’s probabilistic distribution function with considering the leakage effect created better results. This innovative approach holds the potential to create comprehensive zonal water consumption patterns tailor-made for different population sizes.

Water distribution network calibration for unreported leak localization with consideration of uncertainties

Water distribution network calibration for unreported leak localization with consideration of uncertainties

Stable set reformulations for the degree preserving spanning tree problem

Let G=(V,E) be a connected undirected graph and assume that a spanning tree is available for it. Any vertex in this tree is called degree preserving if it has the same degree in the graph and in the tree. Building upon this concept, the Degree Preserving Spanning Tree Problem (DPSTP) asks for a spanning tree of G with as many degree preserving vertices as possible. DPSTP is very much intertwined with the most important application for it, so far, i.e., the online monitoring of arc flows in a water distribution network, what serves us as an additional motivation for our investigation. We show that degree preserving vertices correspond to a stable set of a properly defined DPSTP conflict graph. This, in turn, allows us to use valid inequalities for the Stable Set Polytope (SSP) and thus strengthen the DPSTP formulations previously suggested in the literature. In doing so, the Branch-and-cut and Benders Decomposition algorithms suggested for these formulations are greatly enhanced. Additionally, as a further contribution, we also introduce a new and very challenging set of DPSTP test instances, given by graphs that closely resemble water distribution networks. For these instances, the new algorithms very clearly outperform all previous DPSTP algorithms. They not only run faster than their competitors but are also less dependent on good initial DPSTP primal bounds. For previous DPSTP test sets, the new algorithms lag behind the best existing algorithm for them, which is based on Lagrangian relaxation. However, as compared to additional DPSTP previous algorithms that do not benefit from SSP inequalities, the new ones come much closer to the Lagrangian relaxation one.

Chance constrained optimal power-water flow: An iterative algorithm assisted by deep neural networks

The optimal power-water flow (OPWF) is a fundamental tool for operating integrated power and water networks. To address uncertainties in renewable energy and load demands, researchers have used chance-constrained models to investigate OPWF under uncertainty. However, existing work has limitations, such as oversimplified network representations, intractable problem formulations, and high computational burdens. This paper presents a chance-constrained OPWF formulation that incorporates detailed network constraints and stochastic uncertainties from wind speeds and electricity/water demands. By leveraging the concept of quantiles of stochastic variables and convexification techniques, the model is recast into a deterministic mixed-integer second-order cone programming (MISOCP) problem with uncertain margins. To efficiently solve this problem, we propose a novel iterative algorithm assisted by deep neural networks (DNNs). The algorithm integrates two DNNs: one designed to solve power and water flow equations, accelerating the probabilistic evaluation of uncertain margins; and the other to predict pipeline flows and refine variable ranges, facilitating a warm-start approach to the MISOCP model. Case studies conducted on the IEEE 123-node feeder coupled with a 67-node water distribution network validate the two DNNs and the overall iterative algorithm. The findings demonstrate that the proposed DNN-assisted approach effectively balances system security and economy. Compared to existing algorithms, the relative error of the optimization solution is less than 0.03%, with a speedup ratio exceeding 200.

A stepwise fast leakage localization method applying the strategy of dynamic area narrowing down for large-scale water distribution network

ABSTRACT Urban water distribution networks (WDNs) are facing serious leakage problems. As cities expand, the leakage localization burden on large WDNs gradually increases. Although methods have been widely researched, there is a lack of studies and successful applications for large-scale WDNs. To deal with this, a stepwise fast leakage localization Method, SFLLM, utilizing the `dynamic area narrowing down (DAND)' strategy and coupled leakage features (CLF) is proposed. The SFLLM includes a fast-and-dynamic stage using the DAND strategy to reduce the potential leakage area and an accurate localization stage. Only partial representative candidate locations are required to simulate leakages by DAND, and meantime CLF is used to analyze the leakage similarities so that the localization accuracy and efficiency can be improved. SFLLM was tested on a benchmark WDN, saving more than 88% of simulation by DAND strategy and achieving localization in 11 seconds. The results also proved the enhanced performance of CLF in ensuring the stability of the accuracy against various types of uncertainties that may occur in real WDNs. Moreover, three real burst leaks in an actual large-scale WDN were localized within 205 m in about 22 minutes by SFLLM, showing the method's reliable applicability in guiding field leak exploration.

The four misses of agricultural water husbandry in the Maghreb

AbstractRecurrent droughts plaguing the Maghreb geography have impacted dramatically on the yet scarce renewable water resources. This situation intensified tensions between water users, namely urban, industrial and agricultural sectors. At this point, agricultural water management in large‐scale irrigated schemes is under scrutiny since more than two‐thirds of the Maghreb mobilized water resources are used for agricultural purposes. However, this sector's performance is undermined by mismanagement, misuse, mispricing and misgovernance of agricultural water. From low irrigation water transport efficiencies, low water use efficiency at field level and low valuation of irrigation water by farmers, to nonreasoned pricing policies and lack of good governance, agricultural water husbandry is suffering a number of setbacks. This situation endangers the sustainable development of large‐scale irrigation schemes and could deprive Maghreb countries of the contribution of irrigated agriculture to countries' GDP and food security. These constraints were thoroughly analyzed, and a set of remedies was discussed that included restoration of water transport canals and water distribution networks, improvement of cropping systems, crop water use efficiency, extension services, reasoned subsidies and irrigation water pricing. Only by redirecting water husbandry in the large‐scale irrigation schemes, Maghreb countries can achieve sustainable development.

Water Supply Management Through an Innovative Dashboard Solution

The increased public awareness and stronger restrictions have led to a rising recognition of the significance of advanced technology, especially Geographic Information Systems (GIS), in the management of water resources. The use of GIS in water management is highlighted in this work, with particular attention paid to pollution control, hydrologic modelling, and analytical water supply systems. Furthermore, by combining six conditioning factors and applying vulnerability analysis and logistic regression, it offers a novel approach to leak detection in water distribution networks (WDNs). In order to support preventive maintenance and resource allocation, the model predicts vulnerable locations properly. It is also suggested to use machine learning algorithms to optimise turbine maintenance schedules as part of a predictive maintenance framework for hydroelectric power facilities. High accuracy rates are attained by the framework, which finds important variables for maintenance prediction. These methods provide useful instruments for effective management of water resources overall.

Optimizing sampling location for water quality degradation monitoring in distribution systems: Assessing global representativeness and potential health risk

Selecting the optimal monitoring points in a water distribution network is challenging due to the complex spatiotemporal variability of water quality degradation. The lack of a standardized methodology for monitoring point selection forces operators to rely on general recommendations, historical data and professional experience, which can mask water quality problems and increase the risk to consumers. This study proposes a new methodology to optimize the selection of monitoring points in distribution networks. The method considers the spatiotemporal degradation of water quality, the definition of representative zones and two selection criteria: global representativeness and potential health risk. Representative zones were determined for each node of the network based on hydraulic paths and their water quality spatial variability. Part of the distribution network in Quebec City, Canada was used as the case study, in which four water quality parameters were investigated: free chlorine residual (FRC), heterotrophic plate counts (HPC), trihalomethanes (THMs) and haloacetic acids (HAAs). Seasonal variabilities (summer and winter) were also analyzed. The results obtained for the two criteria and for both seasons were compared, and methodological and practical recommendations were established for dynamic monitoring programs that respond to the needs of operators.

Sinkhole Risk-Based Sensor Placement for Leakage Localization in Water Distribution Networks with a Data-Driven Approach

Sinkhole Risk-Based Sensor Placement for Leakage Localization in Water Distribution Networks with a Data-Driven Approach

Spatial distribution and health risk assessment of heavy metals in drinking water in Nikshahr and Iranshahr cities of Iran

ABSTRACT The presence of high concentrations of certain heavy metals (HMs) in drinking water poses a serious threat to the health of consumers. This work was done to investigate the concentration of heavy metals such as Arsenic (As) cadmium (Cd), chromium (Cr), iron (Fe), mercury (Hg), nickel (Ni), lead (Pb) and cobalt (Co) in the water resources of two cities of Iranshahr and Nikshahr in the south of Sistan and Baluchistan province. For this aim, 24 samples of drinking water sources were collected and the concentration of heavy metals was determined by inductively coupled plasma-mass spectrometry (ICP-MS). The results of samples of these networks showed that the average concentration of Pb (11.25 μg/L), Hg (10.33 μg/L), As (10.62 μg/L) and Cd (3.45 μg/L) far exceeds the standard of the World Health Organisation. The average concentration of heavy metals in the drinking water of Iranshahr and Nikshahr is respectively Fe>Pb>As>Hg>Ni>Cr>Co>Cd and Fe>Pb>As> Ni > Cr >Co>Cd> Hg. Potential sources of pollution in this study are attributed to infiltration of industrial effluents and agricultural runoff, which is a significant threat to groundwater quality. The calculated hazard quotients (HQ(values for non-carcinogenic risk show that this value is less than 1 in most metals, but the hazard index)HI) in children is greater than 1, and non-carcinogenic risk is evident. HI was obtained for children 5.4 and adults 2.6. However, the risk of carcinogenics in children and adults is equal to 2.66 E-05 and1.3E–6, respectively, and the increase in the risk of cancer in children is higher due to the consumption of drinking water. Therefore, it is necessary to protect water sources, water treatment and monitor more water distribution networks based on the water safety plan.

Faktor-faktor yang mempengaruhi saluran distribusi PT. Tirta Investama Surabaya

The development of the bottled drinking water (AMDK) business has successfully embedded the values and perspectives of AMDK in Indonesia. In marketing the distribution team has an extensive mineral water distribution network throughout Indonesia. The company has a role to maintain customers by selling distributing products must be done well, distributing products to outlets in a timely and orderly manner, maintaining and maintaining relationships with customers so that they do not switch from our products. The research method used by researchers is quantitative research. The sample in this study is the annual report of PT Tirta Investama for the last three years (2020-2022). The analysis techniques used in this study are descriptions of research data, multiple linear regression tests, normality tests and hypothesis tests. In hypothesis testing there are three tests, namely the t test, F test and determination test. The results of the analysis that have been described answer the objectives and problem formulation of this study. The application of distribution channels at PT Tirta Investama Surabaya is Market coverage can partially affect distribution channels at PT Tirta Investama Surabaya. PT Tirta Investama Surabaya and Goods can partially influence the distribution channels at PT Tirta Investama Surabaya. PT Tirta Investama Surabaya.

A graph-based method for identifying critical pipe failure combinations in water distribution networks

ABSTRACT Water distribution networks (WDNs) are critical infrastructures prone to vulnerabilities which lead to failures. Identifying vulnerable components, especially multiple pipe failure combinations, is crucial for effective management and ensuring high reliability. Hydraulic simulations are commonly used for analysing the criticality of WDN, but are time-consuming and highly data-reliant, limiting the number of testable combinations. To address these limitations and constraints, a graph-based method is proposed to quantify the impact magnitude of multiple pipe failure scenarios on performance, enabling the identification of critical combinations. The proposed graph-based approach utilizes structural and topological characteristics of WDNs as well as spatial demand distribution to replicate hydraulic behaviour. The accuracy of the approach is assessed by testing it on three case studies with various pipe failure combinations, and the results are compared with hydraulic analyses. The results demonstrate a strong correlation (Spearman coefficient > 0.75) between graph-based ranking and state-of-the-art hydraulic-based ranking. Additionally, the method exhibits a significant computational gain factor of greater than 30 compared with the hydraulic-based method, rendering it valuable for actively exploring a wide range of critical pipe failure combinations and devising countermeasures. Furthermore, a hybrid-based method that integrates both the graph and hydraulic-based methods is proposed for enhanced accuracy and robust assessments.

Composition and Transformation of Halogenated Products Contained in Drinking Water Transported through the Urban Water Distribution Network

Analysis of the composition and transformation of organic compounds in water obtained at a surface water intake of a large urban agglomeration (by the example of the city of Ufa) and used to arrange water supply for the population and the industrial sector has been carried out. Using gas and liquid chromatography with various types of detection, more than 250 organic compounds in the supplied water have been determined. It has been revealed that when transporting water, the concentration of organic compounds of technogenic nature (phthalates, polyaromatic hydrocarbons, alkylbenzenes, etc.) does not change. It has been concluded that in remote, dead-end and stagnant zones of the urban water distribution network, the presence of additional organic pollutants not identified in drinking water leaving water treatment facilities, has not been detected.

A demand-based three-stage seismic resilience assessment and multi-objective optimization method of community water distribution networks

This paper proposes a three-stage resilience concept that takes into account reliability, vulnerability and recovery resilience of water distribution networks (WDNs) from three phases, namely, pre-disaster preparation (PDP) phase, disaster response (DR) phase and the post-disaster recovery (PDR) phase. Based on the three-stage resilience concept, the initial performance, robustness and multi-objective optimization recovery method are proposed. In PDP phase, based on the reliability concept and the user demand, the initial performance of WDNs is defined, and the method is proposed based on Monte Carlo simulation method combined with seismic fragility of pipelines. In DR phase, a comprehensive quantitative index is proposed to quantify the robustness of WDNs by integrating the structural attributes, topological attributes and operational characteristics of WDNs. Finally, on the basis of the previous two stages, the disaster recovery process of failed pipelines of WDNs is further analyzed, and a multi-objective recovery optimization model of failed pipelines with consideration of users’ demands under the resources constraints is proposed to achieve the resilience enhancement. Through a case, the three-stage resilience assessment optimization enhancement of WDNs is implemented using the improved NSGA-II. The proposed method can be used as a basis for disaster prevention planning and seismic measures.

Feasibility of sustainability separation of drinking water network by groundwater desalination Bajestan urban catchment, Iran

Fresh water supply in arid land is facing many challenges that require proper management of water resources, especially in drinking water. Management of water projects depends on the conditions of each country or region. Bajestan is located in Razavi Khorasan province, Iran. The certain resource of drinking water supply is desalinated of high salinity groundwater from Playa. Government has implemented the project “Separation of Drinking and sanitary water networks by groundwater desalination” since of 2014, by creating “Government Drinking Water Withdrawal Smart Stations (GDWWSS)" in this city. This study evaluates the feasibility of different methods of separation drinking water from sanitary water by considering sustainability dimensions these methods. These methods including of Home Water Desalination Machines, Mobile Packaged Water, GDWWSS (with new design), Private Drinking Water Withdrawal Stations (PDWWS) and Dual Drinking Water Distribution Network (DDWDN). Findings showed, that if just the economic dimension is considered in the drinking water separation project, PDWWS would be the best method but since the social dimension is an integral part of a sustainable project, the most compatible method is DDWDN. The environmental evaluation showed that production of salt water and salt (NaCl) is the most common adverse effect of all the proposed methods, but the DDWDN will produce more salt wastewater. On the other hand, Mobile packaged water method will produce 17,000 tons of PET annually, which will cause biological problems if it is not recycled. Therefore, the best way is to use the water resources of the plain and not use the desalination of salt groundwater resources. Also, to increase people's satisfaction and reduce water consumption with good quality water, DDWDN is suggested.

Emergency monitoring layout model for sudden water pollution accidents in water distribution networks

ABSTRACT The layout of emergency monitoring points (EMPs) in water distribution networks is one of the main measures to ensure the water safety of residents and preserve the sustainable development of cities and society. The impact factors on the layout of EMPs are first analyzed. Then, a new emergency monitoring layout model is presented with the goal of maximizing the number of the excluded possible sources and minimizing the formulated time of emergency response. Finally, taking EPANET Network 3 as a simulation case to verify the proposed model. The results show the new layout model can effectively exclude 60% of the number of possible sources, and shorten 18% of the formulated time of emergency response measure. There are many factors that affect the layout of EMPs, which mainly include the time of arranging EMPs, the latest time to formulate the emergency response measure, the number of emergency monitoring equipment available, and the relative error criteria of pollutant concentration, from apparently to slightly. It provides a new idea for emergency decision-makers to deal with sudden water pollution accidents in water distribution networks.

Identifying Pareto-optimal seismic rehabilitation strategies for water distribution networks considering decision maker’s risk attitudes

Identifying Pareto-optimal seismic rehabilitation strategies for water distribution networks considering decision maker’s risk attitudes

An explainable machine learning approach to the prediction of pipe failure using minimum night flow

ABSTRACT Both minimum night flow (MNF) and pipe failures are common ways of understanding leakage within water distribution networks (WDNs). This article takes a data-driven approach and applies linear models, random forests, and neural networks to MNF and pipe failure prediction. First, models are trained to estimate the historic average MNF for over 800 real-world DMAs from the UK. Features for this problem are constructed from pipe records which detail the length, diameter, volume, age, material, and number of customer connections of each pipe. The results show that 65% of the variation in historic average MNF can be explained using these factors alone. Second, a novel method is proposed to deconstruct the models' predictions into a leakage contribution score (LCS), estimating how each individual pipe in a DMA has contributed to the MNF. In order to validate this novel approach, the LCS values are used to classify pipes based on historic pipe failure and are compared against models directly trained for this. The results show that the LCS performs well at this task, achieving an AUC of 0.71. In addition, it is shown that both LCS and directly trained models agree in many cases on an example real-world DMA.

Rapid response to pressure variations in water distribution networks through machine learning-enhanced data acquisition

ABSTRACT This study investigates rapid dynamic pressure variations in water distribution networks due to critical incidents such as pipe bursts and valve operations. We developed and implemented a machine learning (ML)-based methodology that surpasses traditional slow cycles of pressure data acquisition, facilitating the efficient capture of transient phenomena. Employing the Orion ML library, which features advanced algorithms including long short-term memory dynamic threshold, autoencoder with regression, and time series anomaly detection using generative adversarial networks, we engineered a system that dynamically adjusts data acquisition frequencies to enhance the detection and analysis of anomalies indicative of system failures. The system's performance was extensively tested using a pilot-scale water distribution network across diverse operational conditions, yielding significant enhancements in detecting leaks, blockages, and other anomalies. The effectiveness of this approach was further confirmed in real-world settings, demonstrating its operational feasibility and potential for integration into existing water distribution infrastructures. By optimizing data acquisition based on learned data patterns and detected anomalies, our approach introduces a novel solution to the conventionally resource-intensive practice of high-frequency monitoring. This study underscores the critical role of advanced ML techniques in water network management and explores future possibilities for adaptive monitoring systems across various infrastructural applications.

Fungal and bacterial evaluation in drinking water distribution network and their association with physicochemical parameters

Preventing the growth of microorganisms in water distribution network (WDN) is an important public health priority. In this study, 98 water samples were collected from the Gorgan City (Iran) water distribution network (WDN) to assess fungal and bacterial contamination in WDN and their association with physicochemical parameters. All microbiological and physicochemical tests were performed as described in the standard method. The results showed that 15.3 %, 12.2 %, and 2 % of the sample had total coliforms, Clostridium perfringens and fecal coliforms, respectively; however, no Escherichia coli and fecal streptococci were found in samples. The average count of heterotrophic bacteria and fungi was 203.45 CFU/mL and 6.83 CFU/100 mL, respectively. Seven genera of fungi were isolated, of which Candida (19.4 %) and Aspergillus (11.2 %) were the most prevalent, followed by Penicillium (5.1 %), Rhodotorula (3.1 %), Cladosporium (2 %), Acremonium (2 %) and Alternaria (1 %). The mean and Standard deviation of free residual chlorine (FRC), pH, turbidity, temperature, total organic carbon and electrical conductivity were 0.22 ± 0.27 mg/l, 7.28 ± 0.22, 0.61 ± 0.68 NTU, 19.27 ± 2.83 °C, 0.45 ± 0.17 mg/l, and 953.49 ± 207.54 (µs/cm), respectively. The presence of pathogens fungus is a major public health concern for people with lowered immunity. The present study indicated that using microorganisms with different features provides a more reliable evaluation to assess the microbial growth in WDN. Amongst physicochemical parameters, FRC above 0.8 mg/l prevented the fungal and bacterial pathogens growth in WDN. However, further research is required to measure the risks of trihalomethane formation.