Burst Detection in Water Distribution Systems: The Issue of Dataset Collection

Menapace, Andrea; Zanfei, Ariele; Felicetti, Manuel; Avesani, Diego; Righetti, Maurizio; Gargano, Rudy

doi:10.3390/app10228219

Cited by 25 publications

(11 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two case studies are proposed to carry out the tuning analysis. The first regards an artificially generated time series following the procedure proposed by Menapace et al [31], hereafter called ts1, and the second one is a real WDS in Trentino (Italy), hereafter called ts2. The complete time series are reported in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

“…ts1 has been generated to supply water to approximately 5000 users and random noises have been added to the water request to enhance its variability and producing a realistic final water consumption. For additional details about the procedure, see [31]. Concerning the real consumption time series (ts2) reported in Figures 1b and 2b, it shows a seasonal component with an enhanced daily variability.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Tuning ANN Hyperparameters for Forecasting Drinking Water Demand

2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Tuning ANN Hyperparameters for Forecasting Drinking Water Demand

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the latest years, hydro informatics research played a crucial role in developing new techniques and methodologies for supporting the sustainable management of urban water, dealing with this discipline that "can be thought of as a continuous process of developing and using water data, models and tools, to understand the environment, to engage all stakeholders, and help make decisions that improve society" [3,4]. In the latest decades, artificial intelligence and machine learning have been the object of increased research activity in the hydraulic field [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

An artificial intelligence approach for managing water demand in water supply systems

Zanfei

Menapace

Righetti

2023

IOP Conf. Ser.: Earth Environ. Sci.

Self Cite

View full text Add to dashboard Cite

Water demand management is essential for water utilities, which have the critical task of supplying drinking water from water sources to end-users through the distribution network. Therefore, the water utilities have to make decisions for the current and future functioning of the water distribution system. In this context, the artificial intelligence approach with data-driven methods can be used to develop powerful tools to improve overall water management. In fact, data-driven methods can model water demands for plenty of tasks and applications such as demand forecasting or anomaly detection. In this work, we propose and discuss a practical application of an artificial neural network to model the urban water demand of a water supply system. The flexibility of the proposed method allows the prediction of water demand on different horizons. Moreover, this developed model can effectively support water utilities on different operational schedules and decision tasks.

show abstract

“…Water is a vital and fundamental resource for human health, survival and development. However, the world's water resources continue to be depleted by high customer demand and infrastructural losses and leakages [1][2][3]. Although water leakages continue to increase in developed and developing countries, detecting and repairing them is even more costly for most developing countries experiencing limited financial capacity [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Optimal Pressure Management in Water Distribution Systems: Efficiency Indexes for Volumetric Cost Performance, Consumption and Linear Leakage Measurements

Mathye

Scholz

Nyende‐Byakika

2022

Water

View full text Add to dashboard Cite

Water is a finite resource and should be given the attention it deserves to reduce its depletion through leakages in pipe systems. The authors implemented pressure management strategies linked to fixed and variable discharge (FAVAD), the burst and background estimate (BABE), and orifice principle methodologies to analyze a two-phased comparative method for applying optimal pressure management and its efficiency indexes in measuring volumetric cost performance, consumption, leakage flowrate, linear leakage reduction, infrastructural leakage and leakage cost indices. Using time-modulated smart control pressure reducing valve (PRV) simulation processes, the authors selected Alexandra Township in Johannesburg, South Africa as a case study. The results showed a reduction in head pressure, a reduction in the system input volume (SIV) from 26,272,579 m3 to 21,915,943 m3 and a reduction in minimum night flow (MNF) from 14.01% to 12.50%. The annual estimated nodal system output (NSO) was reduced from 14,774.62 m3 to 12,787.85 m3. The monthly average linear system repairs were reduced from 246 to 177, while the efficiency index percentages of leakage frequency/km/pressure were reduced from 8.31% to 5.98%. At a unit cost of $3.18/m3, the cost of leakages declined from $4,009,315.54 to $2,862,053.10 per month, while average household consumption (AMC) reduced from 36.33 m3 to 24.56 m3. Finally, the linear reduction value R2 for the percentage of the total leakage flowrate (TLFR)/SIV declined from 0.58 to 0.5, whereas the infrastructure leakage ratio (ILI) increased from 4 to 4.3. The results fully demonstrated that optimal pressure management is an alternative way to simulate, estimate, quantify and understand where and how water is lost in a distribution system. The authors propose that the implementation of proactive leakage management and domestic background leakage repair could further assist in reducing the frequency and cost of water leakages.

show abstract

Burst Detection in Water Distribution Systems: The Issue of Dataset Collection

Cited by 25 publications

References 41 publications

Tuning ANN Hyperparameters for Forecasting Drinking Water Demand

Tuning ANN Hyperparameters for Forecasting Drinking Water Demand

An artificial intelligence approach for managing water demand in water supply systems

Optimal Pressure Management in Water Distribution Systems: Efficiency Indexes for Volumetric Cost Performance, Consumption and Linear Leakage Measurements

Contact Info

Product

Resources

About