Proper hydraulic simulation models, which are fundamental to analyse a water distribution system, require a calibration procedure. This paper proposes a multi-objective procedure to calibrate water demands and pipe roughness distribution in the context of an ill-posed problem, where the number of measurements is smaller than the number of variables. The proposed methodology consists of a two-steps procedure based on a genetic algorithm. Firstly, several runs of the calibrator are performed and the corresponding pressure and flow-rates values are averaged to overcome the non-uniqueness of the solutions problem. Secondly, the final calibrated model is achieved using the calibrator with the average values of the previous step as the reference condition. Therefore, the procedure enables to obtain physically based hydraulic parameters. Moreover, several hydraulic models are investigated to assess their performance on this optimisation procedure. The considered models are based either on concentrated at nodes or distributed along pipes demands approach, but also either on demand driven or pressure driven approach. Results show the reliability of the final calibrated model in the context of the ill-posed problem. Moreover, it is observed the overall better performance of the pressure driven approach with distributed demand in scarce pressure condition.

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Many efforts have been made in recent decades to formulate strategies for improving the efficiency of water distribution systems (WDS), led by the socio-demographic evolution of modern society and the climate change scenario. The improvement of WDS management is a complex task that can be addressed by providing services to maximize revenues while ensuring that the quality standards required by national and international regulations are upheld. These two objectives can be fulfilled by utilizing optimized techniques for the operational and maintenance strategies of WDS. This paper proposes a methodology for assisting engineers in identifying water leakages in WDS, thus providing an effective procedure for ensuring high level hydraulic network functionality. The proposed approach is based on an inverse analysis of measured flow rates and pressure data, and consists of three steps: The analysis of measurements to select the most suitable period for leakage identification, the localization of the best measurement points based on a correlation analysis, and leakage identification with a hybrid optimization that combines the exploration capability of the differential evolution algorithm with the rapid convergence of particle swarm optimization. The proposed procedure is validated on a reference hydraulic network, known as the Apulian network.

The evolution of smart water grids leads to new Big Data challenges boosting the development and application of Machine Learning techniques to support efficient and sustainable drinking water management. These powerful techniques rely on hyperparameters making the models’ tuning a tricky and crucial task. We hence propose an insightful analysis of the tuning of Artificial Neural Networks for drinking water demand forecasting. This study focuses on layers and nodes’ hyperparameters fitting of different Neural Network architectures through a grid search method by varying dataset, prediction horizon and set of inputs. In particular, the architectures involved are the Feed Forward Neural Network, the Long Short Term Memory, the Simple Recurrent Neural Network and the Gated Recurrent Unit, while the prediction interval ranges from 1 h to 1 week. To avoid the problem of the Neural Networks tuning stochasticity, we propose the selection of the median model among several repetitions for each hyperparameter’s configurations. The proposed iterative tuning procedure highlights the change of the required number of layers and nodes depending on Neural Network architectures, prediction horizon and dataset. Significant trends and considerations are pointed out to support Neural Network application in drinking water prediction.

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Developing data-driven models for bursts detection is currently a demanding challenge for efficient and sustainable management of water supply systems. The main limit in the progress of these models lies in the large amount of accurate data required. The aim is to present a methodology for the generation of reliable data, which are fundamental to train anomaly detection models and set alarms. Thus, the results of the proposed methodology is to provide suitable water consumption data. The presented procedure consists of stochastic modelling of water request and hydraulic pipes bursts simulation to yield suitable synthetic time series of flow rates, for instance, inlet flows of district metered areas and small water supply systems. The water request is obtained through the superimposition of different components, such as the daily, the weekly, and the yearly trends jointly with a random normal distributed component based on the consumption mean and variance, and the number of users aggregation. The resulting request is implemented into the hydraulic model of the distribution system, also embedding background leaks and bursts using a pressure-driven approach with both concentrated and distributed demand schemes. This work seeks to close the gap in the field of synthetic generation of drinking water consumption data, by establishing a proper dedicated methodology that aims to support future water smart grids.

Water distribution system monitoring is currently carried out using advanced real-time control technologies to achieve a higher operational efficiency. Data analysis techniques can be implemented for condition estimation, which are crucial tools for managing, developing, and operating water networks using the monitored flow rate and pressure data at some network pipes and nodes. This work proposes a state estimation methodology that enables one to infer the hydraulic state of the operating speed of pumping systems from these pressure and flow measurements. The presented approach suggests using graph convolutional neural network theory linked to hydraulic models for generating a digital twin of the water system. It is validated on two benchmark hydraulic networks: the Patios-Villa del Rosario, Colombia, and the C-Town networks. The results show that the proposed model effectively predicts the state estimation in the two hydraulic networks used. The results of the evaluation metrics indicate low values of mean squared error and mean absolute error and high values of the coefficient of determination, reflecting high predictive ability and that the prediction results adequately represent the real data.

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