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Prediction of urban water consumption can help to improve the performance of water distribution systems. Despite the obvious presence of uncertainty in measurements and in assumed model types/structures, most of the existing water consumption prediction models are developed and used in a deterministic context. Methods for more realistic assessment of parameter and model prediction uncertainties have begun to appear in literature only recently. A novel application of the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) for the calibration of a water consumption prediction model is proposed here. The model is applied to a case study of the city of Catania (Italy) with the aim to predict daily water consumption. The SCEM-UA algorithm is used to calibrate the parameters of the artificial neural network based prediction model and in turn to determine the associated parameter and model prediction uncertainties. The results obtained using the SCEM-UA ANN approach were compared to the corresponding results obtained using other predictive models developed recently by the authors of the paper. When compared to the these models, the SCEM-UA ANN based water consumption prediction model shows similar predictive capability but also the ability to identify simultaneously the prediction uncertainty bounds associated with the posterior distribution of the parameter estimates.

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This paper investigates the potential of unsteady flow modelling for the simulation of remote real-time control (RTC) of pressure 6 in water distribution networks. The developed model combines the unsteady flow simulation solver with specific modules for generation of 7 pulsed nodal demands and dynamic adjustment of pressure control valves in the network. The application to the skeletonized model of a real 8 network highlights the improved capability of the unsteady flow simulation of RTC compared with the typical extended period simulation 9 (EPS) models. The results show that the unsteady flow model provides sounder description of the amplitude of the pressure head variations at 10 the controlled node. Furthermore, it enables identification of the suitable control time step to be adopted for obtaining a prompt and effective 11 regulation. Nevertheless, EPS-based models allow consistent estimates of leakage reduction as well as proper indications for valve setting 12 under network pressure RTC at a much smaller computational cost.

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Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently. To make combinatorial optimization more efficient, we introduce the Filtering Variational Quantum Eigensolver (F-VQE) which utilizes filtering operators to achieve faster and more reliable convergence to the optimal solution. Additionally we explore the use of causal cones to reduce the number of qubits required on a quantum computer. Using random weighted MaxCut problems, we numerically analyze our methods and show that they perform better than the original VQE algorithm and the Quantum Approximate Optimization Algorithm (QAOA). We also demonstrate the experimental feasibility of our algorithms on a Honeywell trapped-ion quantum processor.