Hydraulic models have been widely used for design, analysis, and operation of water distribution systems. As with all hydraulic models, water demands are one of the main parameters that cause the most uncertainty to the model outputs. However, the calibration of the water demands is usually not feasible attributable to the limited quantity of available measurements in most real water networks. This paper presents an approach to calibration of the demand multiplier factors under an ill-posed condition where the number of measurements is less than the number of parameter variables. The problem is solved using a genetic algorithm (GA). The results show that not only is the GA able to match the calibrated values at measured locations, but by using multiple runs of the GA model, the flow rates and nodal heads at nonmeasured locations can be estimated. Three case studies are presented as an illustration of the problem. The first case study is a small network that demonstrates the calibration model. The ...

Global sensitivity analysis (GSA) provides essential insights into the behavior of Earth and environmental systems models and identifies dominant controls of output uncertainty. Previous work on GSA, however, has typically been under the assumption that the controlling factors such as model inputs and parameters are independent, whereas, in many cases, they are correlated and their joint distribution follows a variety of forms. Although this assumption can limit the credibility of GSA and its results, very few studies in the field of water and environmental modeling address this issue. In this paper, we first discuss the significance of correlation effects in GSA and then propose a new GSA framework for properly accounting for correlations in input/parameter spaces. To this end, we extend the “variogram‐based” theory of GSA, called variogram analysis of response surfaces (VARS), and develop a new generalized star sampling technique (called gSTAR) to accommodate correlated multivariate distributions. We test the new gSTAR‐VARS method on two test functions, against a state‐of‐the‐art GSA method that handles correlation effects. We then apply gSTAR‐VARS to the HBV‐SASK model, calibrated via a Bayesian, Markov chain Monte Carlo approach, for design flood estimation in the Oldman River Basin in Canada. Results demonstrate that accounting for correlation effects can be critically important in GSA, especially in the presence of nonlinearity and interaction effects in the underlying response surfaces. The proposed method can efficiently handle correlations and different distribution types and simultaneously generate a range of sensitivity indices, such as total‐variogram effects, variance‐based total‐order effects, and derivative‐based elementary effects.

Reconstructing transient states presents significant challenges, particularly within complex pipe networks. These challenges arise due to nonlinear behaviours, inherent uncertainties in the system, and limitations in data availability. This work proposed a novel approach employing Physics-Informed Neural Networks (PINN) to reconstruct transient states in pipe networks, even with limited sensor data. To integrate the complex topology of pipe network systems into neural networks, the method integrates the PINN framework with an efficient elastic water column (EWC) model which can be simply formulated across diverse pipe network configurations. The results showed the proposed PINN method can accurately reconstruct the pressure and flow variation at unmonitored locations, even provided with noisy data at a limited number of locations. One of its advantages lies in its ability to effectively capture extreme values that hold potential significance for pipe infrastructure, providing a promising avenue for pipe failure analysis and enhanced safety management. Laboratory experiments have also been conducted to validate the efficacy and reliability of this method, thus further underlining its potential for real-world applications.

Accurate modeling of water distribution systems is fundamental for planning and operating decisions in any water network. One important component that directly affects model accuracy is knowledge of nodal demands. Conventional models simulate flows and pressures of a water distribution network either assuming constant demand at nodes or using a short-term sample of demand data. Given the stochastic behavior of water demand, this assumption usually leads to an inadequate understanding of the full range of operational states in the water system. Installation of sensor devices in a network can provide information about some components in the system. However, the requirement for a reliable water distribution model that can assist with understanding of real-time events over the entire water distribution system is still an objective for hydraulic engineers. This paper proposes a methodology for the estimation of online (near-real-time) demand multipliers. A predictor-corrector approach is developed that (1) predicts the hydraulic behaviors of the water network based on a nonlinear demand prediction model; and (2) corrects the prediction by integrating online observation data. The standard particle filter and an improved particle-filter method that incorporates the evolutionary scheme from genetic algorithms into the resampling process to prevent particle degeneracy, impoverishment, and convergence problems, are investigated to implement the predictor-corrector approach. Uncertainties of model outputs are also quantified and evaluated in terms of confidence intervals. Two case studies are presented to demonstrate the effectiveness of the proposed particle-filter model. Results show that the model can provide a reliable estimate of demand multipliers in near-real-time contexts.

Excessive hydraulic transients in a water distribution system (WDS) can instantaneously damage equipment and infrastructure, while long-term pressure oscillations can contribute to pipe structural deterioration and eventually pipe bursts. It is therefore important to monitor hydraulic transient events in WDSs and locate the source in a timely manner; however, there is a lack of a theoretical basis for optimal sensor placement with regard to real-time transient event detection. This paper investigates the criteria for optimal sensor deployment of high-speed pressure loggers in a WDS and develops a technique for determining the optimal sensor locations. The proposed criteria focus on maximizing the spatial extent of the network within which transient events can be detected and located. A key concept in this work is the locatability of an event, which is defined based on the combination of hydraulic wave propagation theory in networks and the adoption of graphing theoretic concepts, and is based on the existence of unique wave propagation paths from the event to two or more sensors. Two case studies are considered, where the first is a small network that is used to explain the steps of the method, and the second serves as the basis for an extensive numerical study, where it is observed that the proposed method outperforms other approaches and is able to provide the optimum sensor layout for a given number of sensors.