The calibration of drinking water distribution network (DWDN) models is essential to ensure accurate simulation, efficient operation, and informed decision-making. As DWDNs evolve due to seasonal changes, shifting demands, or infrastructure updates, maintaining model accuracy over time becomes increasingly important. However, limited measurement availability and high model complexity make calibration a persistent challenge. To address this, ES-NEAT is introduced, an automatic calibration methodology that combines expert systems (ES) with neuro-evolution of augmenting topologies (NEAT). The method integrates expert knowledge with neural network evolution to efficiently solve high-dimensional calibration problems. ES-NEAT achieves high accuracy under sparse data conditions while keeping computational costs moderate. It also stores calibration knowledge in a structured format, enabling faster and more consistent recalibration over time. This adaptability supports long-term model reliability. The methodology was validated on a benchmark network and a real DWDN in Flanders, Belgium, demonstrating robust performance, efficient convergence, and generalizability across calibration scenarios.

<p>Modelling and, as a consequence, decision-making for water distribution networks is ordinarily performed using the deterministic paradigm in which a single set of input conditions gives rise to a single output “truth”.  Reality is not so accommodating, however, and it is readily apparent that significant uncertainties remain in both our knowledge of the condition and the operating constraints of the network.  These uncertainties include variables such as the effective diameter of pipes, characterised by degradation with age and water chemistry, and the quantities of water demanded by consumers.  Traditionally, where these uncertainties have been accommodated in the decision-making process this has been by considering multiple scenarios to model a small number of model states. </p> <p> </p> <p>The application of probabilistic modelling for water distribution networks has gained significant traction in the literature in recent years – particularly in the context of decision support systems where stochastic parameter sampling is employed to improve the robustness of the obtained solutions.  Nevertheless, the wide interest in probabilistic modelling has yet to be reflected in the emergence of tools to apply this paradigm. </p> <p> </p> <p>This paper introduces VlinderNET a novel tool developed by KWR which seeks to bridge this gap by allowing the user to evaluate and visualize the impact of the manifest uncertainties in the network through the use of probabilistic hydraulic simulation.  VlinderNET permits the specification of complex, cascading Probability Density Functions for the input parameters for a hydraulic simulation.  These PDFs are extensively sampled to produce a wide range of stochastic input variables which are evaluated in a succession of hydraulic simulations which can be parallelized either on a local computer or with cloud support.  The results of the simulations are aggregated and the effects of the uncertain inputs are presented by the tool graphically and spatially both at the component and network level.  The tool further provides an API for third-party applications to integrate the probabilistic paradigm directly into decision support tools in a straightforward and consistent fashion.</p>

Despite preventive measures, turbid (vernacular: “discolored”) distributed drinking water is still a common cause for customer complaints across the world. Discoloration events are caused by the accumulation of particulate matter in drinking water distribution systems (DWDSs) and subsequent remobilization during hydraulic events [1], although uncertainties remain concerning the specific accumulation and transport processes. For Dutch DWDSs, it is plausible that microscopic particles originating at treatment plants contribute substantially to the particulate matter that resides in DWDSs, and that physical processes within the distribution network are cardinal in the subsequent transport during distribution. Aquarellus, a predictive numerical tool has been developed to predict the accumulation of particulate material in DWDSs. It integrates hydraulic calculations using the EPANET toolbox with a particle transport module that is based on a description of gravitational settling, particle stagnation, bed load transport, and resuspension of particles in distribution pipes, depending on the shear stress near the pipe wall [2]. The performance of the multi-core calculations allows for simulating distribution network sizes that are common to Dutch water utilities (100s of km total pipe length). The user can assign the injection of multiple particle species corresponding to temporal patterns at multiple source locations. A graphical user interface handles user IO and the visualization of geographical maps as well as time-dependent build-up of particulate material across the distribution network and within individual pipes. To characterize particle properties (critical input parameters) encountered in Dutch DWDSs, we performed lab experiments on 9 samples from 3 water utilities to determine particle size distributions, mass density, mobility thresholds, and a measure for gravitational settling. Using the outcomes of these lab experiments, a sensitivity test with a range of input parameters was performed in Aquarellus. This helped determine how the variation in the relevant input parameters influence the calculated spatial patterns of accumulated particulate matter ̶ a measure for the discoloration risk. We compared the modeling results to turbidity measurements from systematic cleaning actions in a real-life Dutch distribution network (Spijkenisse). Finally, we will discuss the potential for applying the tool to assist the planning of cleaning actions and monitoring programs.

Optimisation tools are a practical solution to problems involving the complex and interdependent constituents of water resource systems and offer the opportunity to engage with practitioners as an integral part of the optimisation process. A multiobjective genetic algorithm is employed in conjunction with a detailed water resource model to optimise the “Lower Thames Control Diagram”, a set of control curves subject to a large number of constraints. The Diagram is used to regulate abstraction of water for the public drinking water supply for London, UK, and to maintain downstream environmental and navigational flows. The optimisation is undertaken with the aim of increasing the amount of water that can be supplied (deployable output) through solely operational changes. A significant improvement of 33 Ml/day (1% or £59.4 million of equivalent investment in alternative resources) of deployable output was achieved through the optimisation, improving the performance of the system whilst maintaining the level of service constraints without negatively impacting on the amount of water released downstream. A further 0.2% (£11.9 million equivalent) was found to be realisable through an additional low-cost intervention. A more realistic comparison of solutions indicated even larger savings for the utility, as the baseline solution did not satisfy the basic problem constraints. The optimised configuration of the Lower Thames Control Diagram was adopted by the water utility and the environmental regulators and is currently in use.

Despite considerable efforts to improve water management, India is becoming increasingly water stressed due to multiple factors, including climate change, increasing population, and urbanization. We address one of the most challenging problems in the design of water treatment plants: how to select a suitable technology for a specific scenario or context. The process of decision making first requires the identification of feasible treatment configurations based on various objectives and criteria. In addition, the multiplicity of water quality parameters and design variables adds further complexity to the process. In this study, we propose a novel Decision Support Tool (DST), designed to address and support the above challenges. In this user-friendly tool, both Multi-Criteria Decision Analysis (MCDA) and Multi-Objective Optimization (MOO) methods are employed. The integration of MCDA with MOO facilitates the generation of feasible drinking water treatment solutions, identifies optimal options, and ultimately, improves the process of decision making. This implemented approach has been tested for different contexts, including for different types of raw water sources and system implementation scales. The results show that this tool can enhance the process of decision making, supporting the user (e.g., stakeholders and decision makers) to implement the most suitable water treatment systems, keeping in view the trade-offs.

The aging process in water distribution system is one of the main drivers for the reduction of performances in water distribution systems. Furthermore, this conditions usually imply an increase in the maintenance costs that the water utilities have to sustain. In this situation it is required to undertake programmatic strategies in order to guarantee the highest benefit for both the final users and the water companies. An optimal rehabilitation strategy has been proposed in order to take into account the regulatory schemes that have to be abided at the national level, in the water industry.

This paper presents an online, web-based Serious Game developed to investigate end-user behaviour when faced with complex WDS design and rehabilitation problems. SeGWADE (Serious Game for WDS Analysis, Design & Evaluation) couples an innovative and visually attractive interactive front-end with a server-side modelling engine handling real-time hydraulic simulation. A multiplayer online game infrastructure is implemented allowing interactions with a model to be instantly broadcast to both collaborating and competing users. The interactions are recorded and can be subsequently be replayed or analysed through the interface to explore the decision making process in detail. The Serious Game engine is designed to be extensible and can be reconfigured dynamically through an interface allowing the upload of EPANET[1] input files and the parameterisation of different game scenarios permitting a range of games to be developed independently. In addition, the front-end employs adaptive graphics that can switch between HTML5 canvas or WebGL rendering technologies, depending on the client hardware capabilities, in order to deliver the best user experience. The serious game has been evaluated through a classroom-based exercise in which 20 students competed to obtain the best WDS design solution. The results obtained demonstrate a high degree of player engagement with the game. (C) 2016 The Authors. Published by Elsevier Ltd.

In water networks characterized by a significant variation in ground elevations the necessity of pumping water in some areas is complicated by a conflicting requirement to reduce excess pressures in other areas. This and the increasing cost of electricity has led to the use of Pumps-operating-As-Turbines (PATs) devices that can reduce pressure (and leakage) whilst harvesting energy. This paper presents a methodology for optimal water distribution system (WDS) management, driving the optimization by minimizing the surplus pressure at network nodes and the operational pumping costs and maximizing the income generated through energy recovery. The method is based on a highly parallelized Evolutionary Algorithm, employing an hydraulic solver to evaluate hydraulic constraints. Water demands at network nodes are considered as uncertain variables modelled by using a probabilistic approach in order to take into account unknown future demands. The approach is demonstrated in different case studies. Results obtained highlight that the economic benefits of installing PATs for energy recovery in conjunction with a combined pump-scheduling and pressure management regime is especially related to the input network characteristics. Further analysis of the importance of the probabilistic approach and of the influence of the interval time step adopted for the optimization has been evaluated.

A decision support system (DSS) tool for the assessment of intervention strategies in an urban water system (UWS) with an integral simulation model called `WaterMet(2)' is presented. Lists of intervention options and performance indicators are exposed by the DSS for the user to define intervention strategies and metrics for their comparison. The quantitative and risk-based metrics are calculated by WaterMet(2) and risk modules, while the qualitative metrics may be quantified by external tools feeding into the DSS. Finally, a multi-criteria decision analysis approach is employed in the DSS to compare the defined intervention strategies and rank them with respect to a pre-specified weighting scheme for different scenarios. This mechanism provides a useful tool for decision makers to compare different strategies for the planning of UWS with respect to multiple scenarios. The suggested DSS is demonstrated through the application to a northern European real-life case study.

Water systems planning and management share the same roots with gaming, as they rely on concepts in systems analysis, operations research and decision sciences. This paper focuses on Serious Games (those used for purposes other than mere entertainment), with applications in the area of water systems planning and management. A survey of published work on gaming is carried out with particular attention given to applications of Serious Gaming to water systems planning and management. The survey is also used to identify the principal criteria for the classification of Serious Gaming for water related applications, including application areas, goals, number and type of players, user interface, type of simulation model used, realism of the game, performance feedback, progress monitoring and game portability. The review shows that game applications in the water sector can be a valuable tool for making various stakeholders aware of the socio-techno-economic issues related to managing complex water systems. However, the critical review also indicates a gap that exists in the Serious Game application area with the lack of water distribution system games. A conceptually simple, but computationally elaborate new game for water distribution system analysis, design and evaluation (SeGWADE) is presented in this paper. It has a main goal of finding a least-cost design for a well-known benchmark problem, for which the game environment takes the computational and visualisation burden away from the simulation tool and the player. The game has been evaluated in a classroom environment in which a high degree of player engagement with the game was observed, due to its basic game ingredients and activities, i.e., challenge, play and fun. In addition, a clear improvement in learning has been observed in how players attempted to identify solutions that satisfy the pressure criterion with players readily identifying the proximity of the better solutions to the starting, infeasible configuration. Through applications of Serious Gaming such as this, decision makers can learn about the complexity of the water distribution system design problem, experiment safely using a computer model of a real system, understand conflicting objectives (i.e., minimization of cost and satisfaction of minimum pressure) and develop strategies for coping with complexity without being burdened by the limitations of the ICT technology at their disposal.

A decision support system (DSS) tool for the assessment of intervention strategies (Alternatives) in an urban water system (UWS) with an integral simulation model called 'WaterMet(2)' is presented. The DSS permits the user to identify one or more optimal Alternatives over a fixed long-term planning horizon using performance metrics mapped to the TRUST sustainability criteria. The DSS exposes lists of in-built intervention options and system performance metrics for the user to compose new Alternatives. The quantitative metrics are calculated by the WaterMet(2) model, and further qualitative or user-defined metrics may be specified by the user or by external tools feeding into the DSS. A multi-criteria decision analysis approach is employed within the DSS to compare the defined Alternatives and to rank them with respect to a pre-specified weighting scheme for different Scenarios. Two rich, interactive graphical user interfaces, one desktop and one web-based, are employed to assist with guiding the end user through the stages of defining the problem, evaluating and ranking Alternatives. This mechanism provides a useful tool for decision makers to compare different strategies for the planning of UWS with respect to multiple Scenarios. The efficacy of the DSS is demonstrated on a northern European case study inspired by a real-life UWS for a mixture of quantitative and qualitative criteria. The results demonstrate how the DSS, integrated with an UWS modelling approach, can be used to assist planners in meeting their long-term, strategic-level sustainability objectives.

This paper presents a methodological solution to The Battle of Background Leakage Assessment for Water Networks (BBLAWN) competition. The methodology employs two constrained multiple-objective optimization problems and is implemented in the context of a software application for the generic hydraulic optimization and benchmarking of water distribution system (WDS) problems. The objectives are the combined infrastructure and operational costs and system-wide leakage, both to be minimized. In order to accelerate the evaluation of potential solutions, a distributed computing approach permits multiple EPANET solutions to be evaluated in parallel. A pressure-driven demand extension to EPANET assists the optimization in accurately ranking near-feasible solutions and to dynamically allocate leakage demand to nodes. Pressure-reducing valves (PRVs) have been located in two ways: a priori, with respect to the optimization analysis, and a posteriori after the infrastructure optimization to reduce excess pressure and pipe leakage. The latter demonstrates better overall fitness, leading to optimal configurations dominating those obtained with the former. Several temporal resolutions for PRV settings have been evaluated to contrast the optimal solutions with the computational effort required. (C) 2015 American Society of Civil Engineers.

2. DSS Overview A decision support system (DSS) tool for the strategic planning of an integrated urban water system (UWS) over a pre-defined long-term planning horizon is presented here. The DSS evaluates and ranks a number of user defined alternative intervention strategies (IS or simply alternatives) by evaluating their impact on a number of (user defined) UWS performance indicators (PI), all for a number of (user defined) scenarios.

This paper proposes a method to combine linear Model Predictive Control (MPC), a Constraint Satisfaction Problem (CSP) formulation and a Network Aggregation Method (NAM) for the predictive operational control of water pumping in DWNs. The proposed method can produce optimal pumping strategies for complex DWNs in short computation times, while avoiding the need for non- linear programming techniques to cater for non-linear flow-head equations. The proposed approach is simulated using Epanet to represent the hydraulic DWNs. The D-Town benchmark water network is used as a case study.

Extended abstract Urban water services are likely to face challenges in the future, mainly due to population growth, climate change, increasing urbanization and ageing infrastructure. These factors are expected to impose significant strains on the performance of urban water services. This would subsequently decrease the capacity and quality of services in the urban water system (UWS) and thus negatively affect different dimensions of the sustainability framework (i.e. economic, environmental, social, asset and governance) presented by Alegre et al. (2012). Performance of future sustainability in the UWS can be evaluated by using simulation of metabolism-based processes in the urban water cycles over a pre-specified horizon. The WaterMet model developed in the TRUST project quantifies the metabolism-based performance of the integrated UWS (Behzadian et al. 2014a). The integrated modelling of the UWS implies the whole processes and components in an urban area related to water flows as a complex and interrelated system. A mass balance approach of water is followed within the system. Figure 1 illustrates the main flows and storages modelled in WaterMet comprising of four main subsystems.

The methodologies usually adopted in water distribution system redesign problems consider the topology of the network as an input fixed datum; optimisation solely allowing for the duplication/substitution of existing components. In order to contribute to the identification of optimal solutions that may lead to a lower risk of failure to supply the required water, together with a lower redesign cost, this paper proposes a novel methodology which reports the influence of the existing network configuration and its performance. In particular, the redundancy of loops and the robustness of the network topology are investigated by applying an optimisation technique based on a genetic algorithm and by taking into account the random water demand at each node. The methodology presented has been applied to two case studies, in which it considers the influence of the topology on the overall system reliability/risk. The results demonstrate that it is possible to obtain further configurations that are more reliable for a lower redesign cost. The analysis performed highlights the impact of the topology on the search for an optimal solution, which, as a principal conclusion of the work, should be considered among the decision variables taken into account by the optimisation in a redesign problem.

Water reuse schemes in urban water system are assessed in this paper against a number of hydraulic performance indicators.A city metabolism model, WaterMet 2 , is used to evaluate the performance of water reuse schemes.A multi-objective evolutionary algorithm is employed to identify Pareto optimal solutions for the following three objectives: resilience, reliability and total cost.The demonstration of the suggested approach on a real-world case study show the importance of using the resilience index for determining the appropriate schemes.The results suggest, in the case analysed here, the rainwater-harvesting scheme plays a significant role for improvement of resilience index.

In WDS characterized by significant variation in elevation, the necessity of pumping water to higher levels is conflicted by a requirement to reduce excess pressure. A multi-objective optimization methodology is presented to minimize leakage and to minimize the difference between operational pumping costs and income generated through energy recovery by strategically locating in the network Pumps operating As Turbines (PATs), which can act in an analogous fashion to conventional PRVs. The approach is demonstrated on a case study resulting in a clear economic benefit from installing PATs for energy recovery in conjunction with a combined pump-scheduling and pressure management regime.