Search Results (7)

SWANP-AI (Smart Water Network Partitioning with Artificial Intelligence) is a web application with AI natively embedded in its core engines for automated Water Network Partitioning (WNP) of water distribution networks. It is presented as the web-based evolution of SWANP 4.0, whose computational routines have already been tested in operational and research applications. The paper clarifies the full development chain of the platform, from graph-based grouping of candidate District Metered Areas/pressure management Areas (DMA/PMA) to multi-objective boundary pipe optimization and operational decision support. The methodology combines spectral and multilevel k-way partitioning for district generation, NSGA-II for cost–resilience boundary selection, hydraulic simulation through EPANET/WNTR, and AI-supported modules for solution interpretation, sensor placement, natural language editing, and Bayesian leak localization. The application to a real water distribution network shows that SWANP-AI can transform natural language engineering requests into formal optimization tasks, identify hydraulically meaningful candidate interventions, and select balanced solutions through Utopia point analysis, thus reducing manual trial-and-error in DMA/PMA design. The main contribution is a structural generative AI workflow that supports engineers not only in analyzing a network as it is, but also in designing how the network should be partitioned and operated. Full article

Urban water distribution networks (WDNs) are increasingly vulnerable to diverse disruptions, including pipe leaks/bursts and cyber–physical failures. A critical step in a resilience-based approach against these disruptions is the rapid and reliable identification of failures and their types for the timely implementation of emergency or recovery actions. This study proposes a framework for sensor placement and multiple failure type classification in WDNs. It applies a wrapper-based feature selection (recursive feature elimination) with Random Forest (RF–RFE) to find the best sensor locations and employs an Autoencoder–Random Forest (AE–RF) framework for failure type identification. The framework was tested on the C-town WDN using the failure type scenarios of pipe leakage, cyberattacks, and physical attacks, which were generated using EPANET-CPA and WNTR models. The results showed a higher performance of the framework for single failure events, with accuracy of 0.99 for leakage, 0.98 for cyberattacks, and 0.95 for physical attacks, while the performance for multiple failure classification was lower, but still acceptable, with a performance accuracy of 0.90. The reduced performance was attributed to the model’s difficulty in distinguishing failure types when they produced hydraulically similar consequences. The proposed framework combining sensor placement and multiple failure identification will contribute to advance the existing data-driven approaches and to strengthen urban WDN resilience to conventional and cyber–physical disruptions. Full article

Coastal communities face unique challenges in maintaining continuous service from critical infrastructure. This research advances capabilities for evaluating the impact of using wave energy to desalinate water on the resilience of coastal communities. The study focuses on the feasibility of using wave energy conversion to provide drinking water to communities in need and applying resilience metrics to quantify its impact on the community. To assess the feasibility of wave-powered desalination, this research couples the open-source software Wave Energy Converter SIMulator (WEC-Sim) and Water Network Tool for Resilience (WNTR). This research explores variations in both the wave resource (location, seasonality, and duration) and the ability to maintain drinking water service during a disruption scenario by applying the simulation framework to three case studies, which are based on communities in Puerto Rico. The simulation framework provides a contextualized assessment of the ability of wave-powered desalination to improve the resilience of coastal communities, which can serve as a methodology for future studies seeking the integration of wave-powered desalination with water distribution systems. Full article

Water quality problems due to stagnation during periods of low or no demand in buildings, such as the growth of Legionella bacteria, may arise in potable cold and hot water systems. Premise plumbing installations should therefore be designed and constructed to prevent bacterial growth, and then operated to provide satisfactory protection against Legionella. In several building types, over 50% of the total energy usage is connected to hot water production. In large part, this is because hot water systems are maintained at 60 to 70 °C to deter Legionella growth, which may be at odds with sustainability goals. With the latter in mind, recent studies have combined both hydraulics and temperature modelling, obtaining satisfactory prediction results when tested by making digital twins. In the present study, a tool was developed which allows users to see the detailed results of premise plumbing system modelling through a web-based interactive dashboard. The hydraulics are modelled using WNTR, water demands are generated with pySIMDEUM, and temperature is modelled using heat transfer theory for conduction and convection. Some examples are presented to illustrate the extent of the tool, as well as visualising indicators relevant to Legionella growth potential, such as water age and temperature range. This tool can support building managers and designers with improving serviceability, minimising environmental footprint, and providing safe water in new and existing premise plumbing systems. Full article

Water distribution systems (WDSs) need electric power supply to operate their pumps. Long-lasting power outages (blackouts) can disrupt the availability of water for citizens. If the water supply is limited by constrained pumping capacities caused by the blackout, water demand reduction could help preserve this limited supply, while increased water withdrawal, i.e., stockpiling, could deplete it. This study investigates the effects and subsequent uncertainty of demand response, especially stockpiling, on WDSs in a blackout. Therefore, we (i) model residential water demand reduction, regular water demand, and water stockpiling in a blackout, (ii) simulate the effect of the demand response on the WDS of Darmstadt, Germany, and (iii) investigate uncertainty resulting from the demand response and initial states of the WDS at time of the onset of the blackout. The findings indicate that the demand response and initial tank levels are the main sources of uncertainty and that demand-side management bears the potential to improve water service availability during a blackout. Full article

Skeletonization involves simplifying dense large-scale water distribution network (WDN) models by preserving key components that significantly impact network behavior. This study explores five WDNs and implements various skeletonization techniques to help identify a universal criterion for the optimal level of simplification. Results suggest that diverse skeletonization methods affect network topology and hydraulic accuracy. Single-method techniques tend to preserve hydraulic accuracy better but remove fewer pipes, while hybrid methods sacrifice accuracy for simplified topologies and computational time. In addition, a comparative analysis of SkelEpanet and WNTR software shows comparable performance. Ultimately, this work contributes to addressing uncertainties in transferability to real-world networks. Full article

Water distribution networks are often susceptible to pipeline leaks caused by mechanical damages, natural hazards, corrosion, and other factors. This paper focuses on the detection of leaks in water distribution networks (WDN) using a data-driven approach based on machine learning. A hybrid autoencoder neural network (AE) is developed, which utilizes unsupervised learning to address the issue of unbalanced data (as anomalies are rare events). The AE consists of a 3DCNN encoder, a ConvLSTM decoder, and a ConvLSTM future predictor, making the anomaly detection robust. Additionally, spatial and temporal attention mechanisms are employed to enhance leak localization. The AE first learns the expected behavior and subsequently detects leaks by identifying deviations from this expected behavior. To evaluate the performance of the proposed method, the Water Network Tool for Resilience (WNTR) simulator is utilized to generate water pressure and flow rate data in a water supply network. Various conditions, such as fluctuating water demands, data noise, and the presence of leaks, are considered using the pressure-driven demand (PDD) method. Datasets with and without pipe leaks are obtained, where the AE is trained using the dataset without leaks and tested using the dataset with simulated pipe leaks. The results, based on a benchmark WDN and a confusion matrix analysis, demonstrate that the proposed method successfully identifies leaks in 96% of cases and a false positive rate of 4% compared to two baselines: a multichannel CNN encoder with LSTM decoder (MC-CNN-LSTM) and a random forest and model based on supervised learning with a false positive rate of 8% and 15%, respectively. Furthermore, a real case study demonstrates the applicability of the developed model for leak detection in the operational conditions of water supply networks using inline sensor data. Full article