Pipe bursts in water distribution networks cause considerable water losses and lead to potential environmental hazards. Effective burst detection methods enable water companies to repair broken pipes in a timely manner and minimize damage and disruption. A data‐driven detection method is developed and proven for real‐time leak and burst detection in water distribution networks. The method uses a unique integration of disturbance extraction and isolation forest techniques to enable detection of subtle burst signals from normally noisy pressure data. Verification and validation progress from synthetic data to the application to real‐life data from large‐scale open networks. The method is shown to generate comparable levels of detection, with low false positive rates, to other published techniques but without the need to introduce closed valves and expensive flow meters of district meter area structures and the loss of resilience these cause. The method offers the potential to effectively detect bursts from pressure data alone, helping to better manage the distribution of increasingly stressed water resources through aging pipe networks.

Real‐time simulation of water distribution systems (WDSs) has been applied to water resource problems ranging from engineering optimization design and operation planning and management. However, accurate implementation of real‐time simulation of WDS is still a challenging task due to the limited knowledge of the large amount of varying nodal demands and pipe characteristics over the entire network system. This paper presents a self‐adaptive calibration method based on Kalman filter (KF) that takes advantage of the long‐term monitoring data for dual calibration of nodal demands and pipe roughness. Inferential measurements are introduced to avoid linear assumptions of the WDS system and link the hydraulics of WDS with KF. Hence, it is able to employ KF to solve the nonlinear problems in looped water distribution network. By assimilating the long‐term monitoring data and adapting the calibrated parameters to various operating conditions, the framework can reduce the uncertainties caused by measurement errors and quantify the uncertainties by covariance matrixes. In addition, the presented method can help to identify abnormal WDS conditions. Three case studies have been conducted to illustrate the validity of the proposed method and its applications. The results have shown that the proposed framework is reliable and effective in practical applications.

Background: Total suspended solids (TSS) loads carried by stormwater runoff is a major pollutant source on receiving water bodies. Stormwater ponds are widely used for controlling TSS discharge. However, the trap efficiency is not satisfactory because it is affected by many complex factors, which are not fully understood. Therefore, there is a necessity to gain insight into the sediment process in stormwater ponds for optimization design of stormwater ponds. To address this issue, we propose a novel modeling framework based on discrete phase model (DPM), aiming to fully represent the sediment transport, settling, and resuspension at grain scale under time-dependent conditions. Results: In the newly proposed method, heterogeneous characteristics of sediments’ loads, varying flows and sediment loads, settling and resuspension effect at grain scale, time-dependent conditions, and turbulent effect are all well considered. The proposed models have been coded with C language and hooked in computational fluid dynamics software Fluent, and the methods were tested with a case of laboratory experimental setup. Different bed boundary conditions are tested and compared with the observation data for optimization parameters’ identification. The simulation results demonstrated that the physically based DPM with the newly developed method can well reproduce the evolution of sediment transport, settling and resuspension behaviors compared with the scale experiment. Conclusions: The newly proposed method can accurately predict the trap efficiency and temporal–spatial sediment distribution. The decomposition of bed load motion at grain scale is a necessary and valid way to represent the sedimentation process in shallow ponds. The developed model could be a tool to help us gain insight into the sediment transport phenomena at grain scale in shallow tanks since it can provide detailed information which the experiment cannot.