A multi-criteria decision analysis method for water loss management is proposed.The method integrates AHP and TOPSIS methods under fuzzy environment.It is applied to a real water distribution system in a developing country.The prevalent strategies were highly connected to the local conditions. Facing water scarcity conditions water utilities cannot longer tolerate inefficiencies in their water systems. To guarantee sustainable water management one central task is reducing water losses from the supply systems. There are numerous challenges in managing water losses, manifested in a variety of options, their complexities, multiple evaluation criteria, inherent uncertainties and the conflicting objectives and interests of different stakeholders. This study demonstrates the effectiveness of multi criteria decision analysis (MCDA) approaches for decision support in this complex topic. The study covers identifying the key options among a set of options that have been proposed within a framework of strategies to reduce water losses in water distribution systems of developing countries. The proposed methodology was initiated by developing a hierarchical structure of the decision problem that consists of four levels: Overall objective, main criteria, evaluation criteria and options. Different stakeholders were engaged in the process of structuring and evaluating the decision problem. An integrated methodology that combines fuzzy set theory with Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods was then employed. This methodology has the potential to transform qualitative data into equivalent quantitative measures. Fuzzy AHP was used to create weights for main and evaluation criteria, while Fuzzy TOPSIS was used to aid the ranking of options in terms of their potential to meet the overall objective based on the evaluations and preferences of decision makers. The results showed that pressure management and control strategy was the most prevalent one, followed by employing advanced techniques and establishment of district metered areas. Their dominance was highly connected to the local and boundary conditions of the case study. The sensitivity analysis results showed that strongest and weakest options were less sensitive to changes in weights of evaluation criteria, which could be attributed to the strong consensus in strengthening the best option and neglecting the worst option. This study emphasized the successful application of MCDA in dealing with complicated issues in the context of water loss management. It is anticipated that, the integration of this developed framework in the planning policies of water utilities in developing countries can help in conducting better control over water losses.

A framework for water loss management in developing countries under fuzzy environment

Water utilities face a challenge in maintaining a good quality of service under a wide range of operational management and failure conditions. Tools for assessing the resilience of water distribution networks are therefore essential for both operational and maintenance optimization. In this paper, a novel graph-theoretic approach for the assessment of resilience for large scale water distribution networks is presented. This is of great importance for the management of large scale water distribution systems, most models containing up to hundreds of thousands of pipes and nodes. The proposed framework is mainly based on quantifying the redundancy and capacity of all possible routes from demand nodes to their supply sources. This approach works well with large network sizes since it does not rely on precise hydraulic simulations, which require complex calibration processes and computation, while remaining meaningful from a physical and a topological point of view. The proposal is also tailored for the analysis of sectorised networks through a novel multiscale method for analysing connectivity, which is successfully tested in operational utility network models made of more than 100,000 nodes and 110,000 pipes.

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A Graph-Theoretic Framework for Assessing the Resilience of Sectorised Water Distribution Networks

This study focuses on data-driven approaches for burst detection and classifies them into three categories: classification method, prediction-classification method and statistical method. The performance of these methods is discussed. By analysing uncertainty in burst detection, this paper revealed that non-stationary monitoring data and limitations present in these methods challenge the reliability of detection results. Data pre-processing and probabilistic solutions to deal with the uncertainty are summarised. From these findings and discussions, this paper concludes and recommends that: a) data-driven approaches are promising in real-life burst detection and reducing false alarms is an important issue; b) more comprehensive performance evaluation might be necessary, in particular regarding detectable burst size; c) further research on new methods employing multivariate analysis and a new category based on clustering analysis would be beneficial to tackle uncertainty; d) more focus on the use of...

A review of data-driven approaches for burst detection in water distribution systems

Longitudinal dispersion coefficient (LDC) is known as the most remarkable environmental variables which plays a key role in evaluation of pollution profiles in water pipelines. Even though, there is a wide range of numerical models to estimate coefficient of longitudinal dispersion, these mathematical techniques may often come in quite few inaccuracies due to complex mechanism of convection-diffusion processes in pollutant transition in water pipelines. In this research work, to obtain more accurate prediction of LDC, general structure of group method of data handling (GMDH) is modified by means of extreme learning machine (ELM) conceptions. In fact, with getting inspiration from ELM, a novel GMDH method, called GMDH network based on using extreme learning machine (GMDH-ELM), is proposed in which weighting coefficients of quadratic polynomials applied in conventional GMDH are no longer required to be updated either using back propagation technique or other evolutionary algorithms through training stage. In fact, an intermediate parameter is employed to establish a relationship between the input and output in each neuron of the GMDH model. In this way, a well-known and reliable dataset (233 experimental data) related to LDC in water network pipelines, as output vector, is applied to conduct training and testing phases. Through datasets, the Re number, the average longitudinal flow velocity, the friction factor of pipeline and the diameter of pipe are considered as inputs of the proposed approach. The results of GMDH-ELM model indicate a highly satisfying level of precision in both training and testing phases. Furthermore, feed forward structure of GMDH model was improved by particle swarm optimization (PSO) and gravitational search algorithm (GSA) to predict LDC. Through a sound judgment, a comparison is drawn between the performance of GMDH-ELM and other developed GMDH models. Moreover, several empirical equations existing in literature have been applied for comparisons. Overall, results of GMDH-ELM have permissible superiority over the other soft computing tools and conventional predictive models.

Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training Group Method of Data Handling Using Extreme Learning Machine Conceptions

Leakage detection and localization in pipelines has become an important aspect of water management systems Since monitoring leakage in large-scale water distribution networks (WDNs) is a challenging task, the need to develop a reliable and robust leak detection and localization technique is essential for loss reduction in potable WDNs In this paper, some of the existing techniques for water leakage detection are discussed and open research areas and challenges are highlighted It is concluded that despite the numerous research efforts and advancement in leakage detection technologies, a large scope is still open for further research in this domain One such area is the effective detection of background type leakages that have not been covered fully in the literature The utilization of wireless sensor networks for leakage detection purposes, its technical challenges as well as some future research areas are also presented In a general remark, practical application of these techniques for large-scale water distribution networks is still a major concern In this paper, an overview of this important problem is addressed

Towards Achieving a Reliable Leakage Detection and Localization Algorithm for Application in Water Piping Networks: An Overview

The problem of bursts and leakages in water distribution systems has received significantly increased attention over the past two decades. As they represent both an environmental and an economical issue, how to reduce water loss through bursts and leakages is a challenging task for water utilities. Consequently, various techniques have been developed to detect the location and size of leakages. The methods for bursts (or leaks) detection and location can be broadly divided into two main categories, one based on hardware and the other based on software. Hardware-based methods include (i) acoustic detection methods such as listening rods, leak correlators, leak noise loggers and (ii) non-acoustic detection methods such as gas injection, ground penetrating radar technology and infrared photography. Software-based methods make use of the data collected by real-time pressure and/or flow sensors and several artificial intelligence techniques and statistical data analysis tools, including (i) methods based on numerical modeling methods, such as inverse transient analysis, time domain analysis and frequency domain analysis, and (ii) some non-numerical modeling methods, such as artificial neural networks, Bayesian inference systems, the Golden section method, and Kalman filtering. In this article, the authors describe the methods for pipe network burst location and detection, summarize the features of each method, and propose a suggestion for future work.

A review of methods for burst/leakage detection and location in water distribution systems

A practical multi-objective optimization sectorization method for water distribution network.

A new method using artificial immune network is presented to identify pipe burst in water-distribution systems. Burst detection is considered as the problem of pattern recognition in the proposed method. An artificial database that includes information on burst events (BEs) is first established. Using the clonal selection algorithm, the artificial immune network is constructed based on the principle of immune system. The burst location is finally identified using the nearest neighbor method. Three offline case studies are illustrated in detail to evaluate the current method. A total of five possible burst locations are identified from 34 nodes in Case Study 1, whereas four possible burst locations are identified from 77 nodes in Case Study 2. The results derived from the first two case studies show that the method can identify the possible burst areas, including the true burst location, using model-simulated results. The data derived from real BEs in Case Study 3 are used to evaluate the proposed ...

Burst Detection Using an Artificial Immune Network in Water-Distribution Systems

As one of the most important component of Non-Revenue Water (NRW), apparent loss exists in all kinds of water distribution system but hardly can be assessed due to its complexity and invisibility. Aimed to analyze the different component of apparent loss on the basis of the characteristic of flow meters in district metered areas (DMA), an effective methodology to calculate the three primary parts of NRW is presented in this paper. In particular, the real loss assessment can be carried out by Minimum Night Flow (MNF) analysis; while the unauthorized consumption can be identified by consuming variation curve; finally, the metering error is calculated by the genetic algorithm with the integrated model based on IWA water balance audit. Application of this method in water distribution network of a steel-making enterprise improves its significance in controlling NRW of DMA.

Apparent Losses Analysis in District Metered Areas of Water Distribution Systems

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2017WR022147

Kunlun Xin

Papers

A review of methods for burst/leakage detection and location in water distribution systems

A practical multi-objective optimization sectorization method for water distribution network.

Burst Detection Using an Artificial Immune Network in Water-Distribution Systems

Apparent Losses Analysis in District Metered Areas of Water Distribution Systems

Self-Adaptive Calibration of Real-Time Demand and Roughness of Water Distribution Systems