Leak Prediction Model for Water Distribution Networks Created Using a Bayesian Network Learning Approach

Water leakage in water distribution systems (WDSs) can bring various negative economic, environmental, and safety effects. Therefore, predicting water leakage is one of the most crucial tasks in water resource management; however, it is also one of the most challenging ones. Previous leakage-related studies have only focused on detecting existing leaks. This paper presents a novel model using expert structural expectation–maximisation, for predicting water leakage in WDSs. The model can take into account the uncertainty of leakage-related factors and balance the contribution of monitoring data and prior information in a Bayesian learning process to maximise leakage prediction accuracy. Moreover, the proposed method can indicate the most crucial factors affecting water leakage. The results of this study could benefit water utilities by aiding them in establishing an effective leakage control plan to minimise the risk of water leakage. A case study is presented to demonstrate the robustness and effectiveness of the proposed method.     

An Optimization Strategy for Water Distribution Networks

An optimization strategy based on head losses minimization is developed for the least cost design of water distribution networks. A new weighting approach is suggested for calculating the initial flow distribution and optimum pipe diameters of the weighted flow distribution is presented by using least square method. In the mean time homogenous and isotropous head losses are maintained with implications of head loss path choice. The model is employed for designing and/or modifying pipe sizes while the classical Hardy-Cross network solver is used to balance the flows. The whole algorithm is programmed and applied to a two-looped network selected from the literature and the results are presented on a comparative basis. A FORTRAN software with the necessary steps in the flow chart is written for the optimization calculations in this paper.     

Parameter Estimation in Water Distribution Networks

Estimation of pipe roughness coefficients is an important task to be carried out before any water distribution network model is used for online applications such as monitoring and control. In this study, a combined state and parameter estimation model for water distribution networks is presented. Typically, estimation of roughness coefficient for each individual pipe is not possible due to non-availability of sufficient number of measurements. In order to address this problem, a formal procedure based on K-means clustering algorithm is proposed for grouping the pipes which are likely to have the same roughness characteristics. Also, graph-theoretic concepts are used to reduce the dimensionality of the problem and thereby achieve significant computational efficiency. The performance of the proposed model is demonstrated on a realistic urban water distribution network.     

城市供水管网普查技术方法初探

对城市庞大的供水管网进行普查是一项复杂的系统工程。通过对武汉市武昌区供水管网的普查工作 ,探索和总结了一套较有成效的工作方法和程序。     

GA-ILP Method for Optimization of Water Distribution Networks

Optimization of water distribution networks has been of central importance for recent decades. Genetic Algorithms (GA) are the most famous metaheuristics widely used for this purpose with great success. However, the fact that GA basically requires a large number of computations, has led to investigate for faster solvers. In this research, a new approach is proposed in which a simple GA is linked with the Integer-Linear Programming (ILP) method resulting in a hybrid optimization scheme. Using the mathematical method of ILP, the search space is significantly reduced thereby a limited number of evaluations are required to achieve a good solution. The approach is applied to two benchmark pipe-networks in order to show its ability in terms of accuracy and speed. The results are then compared with the previous works. The obtained results indicate that the proposed model is computationally efficient, like classic methods, while is still very promising in finding the global optimum like the nature-inspired metaheuristics.     

Pressure Management Model for Urban Water Distribution Networks

A technique for leakage reduction is pressure management, which considers the direct relationship between leakage and pressure. To control the hydraulic pressure in a water distribution system, water levels in the storage tanks should be maintained as much as the variations in the water demand allows. The problem is bounded by minimum and maximum allowable pressure at the demand nodes. In this study, a Genetic Algorithm (GA) based optimization model is used to develop the optimal hourly water level variations in a storage tank in different seasons in order to minimize the leakage level. Resiliency and failure indices of the system have been considered as constraints in the optimization model to achieve the minimum required performance. In the proposed model, the results of a water distribution simulation model are used to train an Artificial Neural Network (ANN) model. Outputs of the ANN model as a hydraulic pressure function is then linked to a GA based optimization model to simulate hydraulic pressure and leakage at each node of the water distribution network based on the water level in the storage tank, water consumption and elevation of each node. The proposed model is applied for pressure management of a major pressure zone with an integrated storage facility in the northwest part of Tehran Metropolitan area. The results show that network leakage can be reduced more than 30% during a year when tank water level is optimized by the proposed model.     

Robust Hierarchical Sensor Optimization Placement Method for Leak Detection in Water Distribution System

Water Resources Management - The quality of sensor placement in water distribution systems (WDSs) has considerable impact on leak detection accuracy and identification efficiency. Sensor-placement...     

Leakage Identification in Water Distribution Networks with Error Tolerance Capability

Leakages in water distribution networks have caused considerable waste of water resources. Thus, this study proposes a novel method for hydraulically monitoring and identifying regions where leakages occur in near-real time. A large network is first divided into several identification regions. To exploit a strong constructive and discriminative power, sparse coding is used, thereby adaptively coding the information embedded in observed pressures efficiently and succinctly. And a linear classifier is trained to determine the most likely leakage regions. A benchmark case is presented in this study to demonstrate the effectiveness of the proposed method. Results indicate that the proposed method can identify leakage events with enhanced tolerance capability for measurement errors. The method is also partially effective for identifying two simultaneous leakages. Certain practical advice in balancing the number of sensors and regions is also discussed to enhance the application potential of this method.

     

A Selective Literature Review on Leak Management Techniques for Water Distribution System

Water Distribution System suffers from leakages causing social and economic costs. There is need of platform to manage water distribution system more efficiently by detecting, localizing and controlling the leakages even before or as soon as they occur, ensuring quality water services to the consumers. Since last two decades, high efforts have been made by researchers for the development of efficient leakage management techniques for reduction of water losses in distribution system. This paper provides a comprehensive analysis on leakage management techniques covering three aspects: leakage assessment, leakage detection and leakage control, with an objective to identify present challenges and future scope in their respected field. Role of smart water technologies for efficient leakages management in pipeline network is also examined and discussed. Conclusion is drawn regarding current leakage management techniques and proposals for future work and existing challenges are also outlined.     

Water Distribution Network Risk Analysis Under Simultaneous Consumption and Roughness Uncertainties

The design of water distribution networks (WDNs) is an optimization problem with minimization of pipes and their associated installation costs as the objective function. In this problem, securing the allowable minimum pressure or the allowable maximum velocity in the demand pattern is important. A reliable long-term system requires a high reliability when first designed. Thus, assessment of the network condition during the operational period, when it is first designed, can be an effective way to increase the network efficiency. In addition, consideration of uncertainty of network parameters is important. This paper develops a probabilistic model based on the Monte Carlo simulation (MCS) method to assess effects of those uncertainties simultaneously in the long-term performance of the network by considering various scenarios for variations of nodal demands and pipe roughness using different values of the coefficient of variation (CV) as the uncertainty measure. Consumption nodal demands and pipe roughness in a benchmark two-loop network are considered as uncertain variables. Calculation of a deterministic performance (failure) index (I _f ) for various generated probabilistic scenarios in the MCS method during a 30-year operational period simulation in this network show that an increase of uncertainty in each variable separately causes a decrease in the deterministically-designed network efficiency. Sensitivity of changing the average value of I _f calculations show a nodal demand deficit of 45 % and a nodal pressure deficit of 61 % during the operational period. This condition shows the necessity of considering uncertain changes of variables simultaneously during the operational period in the design of WDNs.     

Entropy-Based Sensor Placement Optimization for Waterloss Detection in Water Distribution Networks

The work presented herein addresses the problem of sensor placement optimization in urban water distribution networks by use of an entropy-based approach, for the purpose of efficient and economically viable waterloss incident detection. The proposed method is applicable to longitudinal rather than spatial sensing, thus to devices such as acoustic, pressure, or flow sensors acting on pipe segments. The method utilizes the maximality, subadditivity and equivocation properties of entropy, coupled with a statistical definition of the probability of sensing within a pipe segment, to assign an entropy metric to each pipe segment and subsequently optimize the location of sensors in the network based on maximizing the total entropy in the network. The method proposed is a greedy-search heuristic.     

A Branch-and-Bound Algorithm for Optimal Pump Scheduling in Water Distribution Networks

Nowadays water distribution operation systems are accomplished with the aid of qualified professionals who use their experience in order to achieve a satisfactory performance of the several hydromechanical devices, which are part of the system, such as boosters and valves. In general, these operational rules are empirical and the main goal is to assure the availability of water for the population, with no special concerns about saving energy used in pumping systems. Besides, these empirical rules often disregard hours of lower energy rates. There are several research works concerning the developments of operational rules optimization applied to specific water distribution systems. However, in this work, a general optimization routine integrated with EPANET is presented, which allows the determination of strategic optimal rules of operation for any type of water distribution system. Moreover, a Branch-and-Bound algorithm is also used, where finding the global optimal solution is guaranteed, in admissible computational times. The water distribution system used in this work corresponds to a hypothetical network proposed in the specialized literature.     

Method for Extended Period Simulation of Water Distribution Networks with Pressure Driven Demands

This paper proposes a non-iterative method to perform the simulation of water distribution systems with pressure driven demands using EPANET2 without the need to use its programmer’s toolkit. The method works for single period simulation (snapshot) and for extended period simulation (EPS) as well. It is based on the addition of a flow control valve (FCV), a throttle control valve (TCV), a check valve (CV) and a reservoir to each demand node in the network, in addition to a list of simple controls to modify the setting of the FCV and TCV in each time step. The main advantages of this approach are: 1. the source code of EPANET2 is not modified, 2. the toolkit functions are not needed for the simulation and they remain available for further uses, 3. the extended period simulation (EPS) is performed by EPANET2 and it carries tank levels, demand variation and other time-changing variables internally. The performance of the method is tested in two benchmark networks and a real size network with pumps, tanks and a 24 h demand pattern. The results show that the method computed the pressures and outflows accurately and that the computational time required is not significantly higher than a demand driven execution in most cases.     

Nodal Analysis of Urban Water Distribution Networks

There are three methods for analysing the flow and pressure distribution in looped water supply networks (the loop method, the node method, the pipe method), accounting for the chosen unknown hydraulic parameters. For all of these methods, the nonlinear system of equations can be solved using iterative procedures (Hardy–Cross, Newton–Raphson, linear theory). In the cases of the extension or the rehabilitation of distribution networks, the unknown parameters are the hydraulic heads at nodes, and the nodal method for network analysis is preferred. In this paper, a generalised classic model is developed for the nodal analysis of complex looped systems with non-standard network components and the solvability of new problems, along with the determination of the pressure state in the system. In addition, this paper exhibits a different approach to this problem by using the variational formulation method for the development of a new analysis model based on unconditioned optimisation techniques. This model has the advantage of using a specialised optimisation algorithm, which directly minimises an objective multivariable function without constraints, implemented in a computer program. The two proposed models are compared with the classic Hardy–Cross method, and the results indicated a good performance of these models. Finally, a study is performed regarding the implications of the long-term operation of the pipe network on energy consumption using these models. The new models can serve as guidelines to supplement existing procedures of network analysis.     

Enhancing Knowledge in Water Distribution Networks via Data Assimilation

This paper deals with uncertainty estimation and knowledge enhancement in water distribution networks (WDNs). A new three steps data assimilation approach is introduced, which in combination with multi-objective optimization, allows selecting effective and affordable monitoring networks. An innovative cascade of Ensemble Kalman Filters is used to assimilate the information deriving from sensors measuring pressure heads, flow in pipes and demands, with the objective of increasing knowledge while preserving at the same time the structural relationships among state variables. Selection of the most appropriate and economically affordable measurement network, is then based on the derivation of a Pareto front using the NSGA-II algorithm in conjunction with the data assimilation approach. The front is obtained by compromising between the overall sensors cost and the uncertainty reduction (or knowledge enhancement), which is expressed as a function of the Total Variance of state variables. The operational use of the proposed data assimilation approach as well as the effectiveness of the chosen observation network is also demonstrated by showing the reduction of uncertainty deriving from successive assimilations of real-time observations.     

Uncertainty Analysis of Transient Flow in Water Distribution Networks

For transient analysis of a pipe network, the unsteady flow governing equations should be solved to obtain the extreme pressure heads in the system, which may be faced with several uncertainties. To evaluate that to what extent the input uncertainties can affect the system responses, a simulation model based on the fuzzy sets theory is introduced. For this purpose, triangular fuzzy numbers are used to represent the input uncertainties. Then, to obtain the extreme pressure heads in each location of the network and at each level of uncertainty, four independent optimization problems are solved. In these problems, the nodal maximum and minimum pressure heads are the objective functions and the simulation parameters are the decision variables. Accordingly, for fuzzy analysis of a pipe network, a complicated many-objective optimization problem arises. To solve the problem efficiently a many-objective genetic algorithm is coupled to the transient simulation model. To speed up the analysis, a transient simulation model in the frequency domain is used. The proposed model is applied to a pipe network and the results are discussed. The model is found computationally fast and promising for real applications.     

Calibration of Design Models for Leakage Management of Water Distribution Networks

Water Resources Management - Water losses in urban water distribution networks (WDN) accelerate the deterioration of such infrastructures. The enhanced hydraulic modelling provides a...     

A Neurofuzzy Decision Framework for the Management of Water Distribution Networks

Among the most important components of sustainable management strategies for water distribution networks is the ability to integrate risk analysis and asset management decision-support systems (DSS), as well as the ability to incorporate in the analysis financial and socio-political parameters that are associated with the networks in study. Presented herein is a neurofuzzy decision-support system for the performance of multi-factored risk-of-failure analysis and pipe asset management, as applied to urban water distribution networks. The study is based on two datasets (one from New York City and the other from the city of Limassol, Cyprus), analytical and numerical methods, and artificial intelligence techniques (artificial neural networks and fuzzy logic) that capture the underlying knowledge and transform the patterns of the network’s behaviour into a knowledge-repository and a DSS.     

A Prioritization Model for Rehabilitation of Water Distribution Networks Using GIS

Decision-making for the rehabilitation of water distribution networks in the traditional procedure is based on some simple indices such as the number of incidents while several mechanical, hydraulic and qualitative factors are involved in this process. Evidently, making decision on the rehabilitation of water networks seems to be very difficult as the number of factors increases and they interact with each other. The main objective of this research is to prepare, implement and evaluate a conceptual model to prioritize the rehabilitation of pipes based on different scenarios with respect to the combination effects of basic factors in physical, hydraulic and experimental categories. In order to organize the wide range of data to be used in decision-making models, including the plans aimed for pipe replacement, it is necessary to use geographical information systems (GIS). By determining and introducing the factors involved in the rehabilitation of water networks, this research aims to provide an integrated model consisting of conceptual, GIS, hydraulic analysis and the breakage models to prioritize the rehabilitation schemes. By using the data provided from a real network, the advantages of the proposed methodology are evaluated. Based on the obtained results, age factor, among all the other physical parameters, and pressure, among the hydraulic factors, have the greatest influence in outlining the final rehabilitation scenario. The importance of the pipe length has decreased considerably as well. Furthermore, it can be concluded that rehabilitation management of pipe networks can be optimized by using this methodology.     

Utilizing Matrix Completion for Simulation and Optimization of Water Distribution Networks

Water Resources Management - Simulation of water distribution networks (WDNs) constitutes a key element for the planning and management of water supply systems. The literature presents different...