Smart Technologies for Urban Water Systems

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Urban Water Management".

Deadline for manuscript submissions: closed (10 August 2023) | Viewed by 15438

Special Issue Editors

Dipartimento di Ingegneria, Università degli Studi di Ferrara, via Saragat 1, 44121 Ferrara, Italy
Interests: water demand modelling and forecasting; water consumption smart metering and end-uses characterization; optimal management of water distribution systems; leakage detection; transient analysis of water distribution systems
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Department of Civil and Environmental Engineering, The University of Perugia, via G. Duranti 93, 06125 Perugia, Italy
Interests: time- and frequency-domain modelling of transients in pressurized pipe systems; fault detection in water transmission mains; transient analysis of water distribution systems; leak characterization; use of machine learning for fault detection in pressurized pipe systems
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Dipartimento di Ingegneria Civile e Architettura, Università degli Studi di Pavia, via Ferrata 3, 27100 Pavia, Italy
Interests: water distribution modelling; protection of water distribution networks from contamination events; urban drainage modelling; real-time control; sediment transport in sewers; sustainable solutions for urban drainage systems; flood control in urban areas; modelling of landslides
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Department of Civil Engineering and Architecture, University of Calabria, Via Ponte Bucci, 87036 Rende, Italy
Interests: water distribution system; calibration of hydraulic network; WDS management; leakage detection; coastal engineering
Dipartimento di Ingegneria Civile e Architettura, Università degli Studi di Pavia, via Ferrata 3, 27100 Pavia, Italy
Interests: smart water systems; water distribution network modelling; management and monitoring; urban drainage systems; hydroinformatics; complexity science
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Water Engineering Laboratory, Department of Civil and Environmental Engineering, University of Perugia, I-06125 Perugia, Italy
Interests: pipeline systems; advanced anomaly detection in pipe systems; transient dynamics analysis
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Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Via G. Di Biasio 43, 03043 Cassino, Italy
Interests: water demand modelling; probabilistic analysis of water consumption; water distribution systems rehabilitation/design and management; water systems optimization modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Thanks to the widespread implementation of digital information and communication technology (ICT), our cities are progressively turning into “Smart Cities”as the critical infrastructure components and services, including city administration, education, healthcare, public safety, real estate, transportation, and utilities become more intelligent, interconnected, and efficient. Among the various fields of application, ICT can be successfully implemented to improve monitoring, control and management of urban water systems, namely water distribution and drainage networks. The application of ICT enables constructing a digital twin capable of following the operation of the original system in real time. This yields significant benefits in ordinary operational scenarios for various purposes, including management of:

  • demand, service pressure, leakage and pumping systems in water distribution networks;
  • pollutants and combined sewer overflows in urban drainage networks;

Significant benefits can also be achieved in extraordinary scenarios such as those associated with the identification of anomalous events, e.g., contamination and pipe bursts.

As the digital transition is in progress, increasingly modern and sophisticated algorithms, devices and technologies are being developed and deserve to receive the attention of the scientific audience of the journal “Water”, in the context of the present Special Issue.

Dr. Stefano Alvisi
Dr. Caterina Capponi
Prof. Dr. Enrico Creaco
Dr. Attilio Fiorini Morosini
Dr. Carlo Giudicianni
Dr. Silvia Meniconi
Dr. Carla Tricarico
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart water grids
  • advanced metering infrastructures
  • Information and Communication Technologies (ICT)
  • Internet of Things (IoT)
  • low-cost monitoring technologies
  • hydroinformatic
  • sensor networks
  • non-intrusive load monitoring
  • real-time monitoring
  • dynamic and adaptive control
  • artificial intelligence technologies
  • wireless communication technologies
  • big data analysis
  • machine learning techniques
  • data-driven approaches
  • nowcasting and forecasting models
  • advanced automation
  • cloud computing
  • digitalization
  • cyber-security
  • time series analysis and synthetic patterns generation
  • optimal devices' placement
  • rainwater harvesting
  • energy recovery
  • life cycle assessment (LCA)
  • smart asset management
  • integrated urban water management
  • smart water management
  • transient test-based techniques
  • fault detection

Published Papers (9 papers)

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Research

17 pages, 3444 KiB  
Article
Multivariate Regression Models for Predicting Pump-as-Turbine Characteristics
Water 2023, 15(18), 3290; https://doi.org/10.3390/w15183290 - 18 Sep 2023
Cited by 1 | Viewed by 762
Abstract
Installing pumps as turbines (PaTs) in water distribution networks can recover otherwise wasted energy, as well as reduce leakage caused by high water pressure. However, a barrier to their implementation is the lack of information on their performance in turbine mode. Previous studies [...] Read more.
Installing pumps as turbines (PaTs) in water distribution networks can recover otherwise wasted energy, as well as reduce leakage caused by high water pressure. However, a barrier to their implementation is the lack of information on their performance in turbine mode. Previous studies have proposed models to predict PaT characteristics based on pump best efficiency points (BEPs), using regressions with one or two dependent variables, or more complex artificial neural networks (ANNs). While ANNs were found to improve the accuracy of predictions, these models are known to be unstable with small datasets. Other types of regressions with multiple variables have not been explored. Furthermore, because only small datasets are available to train these models, multivariate regression methods could yield better results. The present study develops multivariate regression models to predict BEPs and characteristic curves of PaTs. A database of 145 BEPs and 196 characteristic curve PaT experimental records was compiled from previous literature. Twenty-four types of multi-variate regressions, as well as ANN were compared, with dimensioned and dimensionless versions of the datasets. The multivariate regression models consistently outperformed previous models, including ANN. The R2 of the head and efficiency curves were 0.997 and 0.909, respectively. Results also showed that XGB regressors and a dimensionless dataset yielded the best-fit models overall. The high accuracy of the models, combined with their lower computational cost compared to ANN, make them a robust solution for selecting PaTs in practice. Full article
(This article belongs to the Special Issue Smart Technologies for Urban Water Systems)
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14 pages, 2614 KiB  
Article
Service Pressure and Energy Consumption Mitigation-Oriented Partitioning of Closed Water Distribution Networks
Water 2023, 15(18), 3218; https://doi.org/10.3390/w15183218 - 10 Sep 2023
Cited by 1 | Viewed by 661
Abstract
This paper presents the partitioning of the closed water distribution network (WDN) serving the city of Pavia, Italy. As a thus far poorly explored aspect in the scientific literature, clustering for the definition of size and extension of district metered areas (DMAs) and [...] Read more.
This paper presents the partitioning of the closed water distribution network (WDN) serving the city of Pavia, Italy. As a thus far poorly explored aspect in the scientific literature, clustering for the definition of size and extension of district metered areas (DMAs) and of inter-DMA boundary pipes is performed by ensuring that the DMAs respect the altimetric areas of the WDN by leaning on a modified formulation of modularity. To define the boundary pipes to be closed or alternatively fitted with a flow meter for the monitoring of DMA consumption, the dividing is performed with an innovative heuristic algorithm. This technique operates by sequentially implementing the boundary closures that do not cause significant head losses, to obtain an approximation of the Pareto front in the trade-off between number of flow meters installed and WDN reliability. In the last part of the work, the pumps present in the network are assumed to be equipped with the variable speed drive, and their hourly settings are optimized to regulate service pressure. Overall, WDN partitioning and pump setting optimization are proven to mitigate the service pressure and energy consumption of the WDN, offering evident and attractive benefits up to about 50% for water utilities. Full article
(This article belongs to the Special Issue Smart Technologies for Urban Water Systems)
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21 pages, 809 KiB  
Article
Customer Complaints-Based Water Quality Analysis
Water 2023, 15(18), 3171; https://doi.org/10.3390/w15183171 - 05 Sep 2023
Cited by 2 | Viewed by 1422
Abstract
Social media has become a useful instrument and forum for expressing worries about various difficulties and day-to-day concerns. The pertinent postings containing people’s complaints about water quality as an additional source of information can be automatically acquired/retrieved and analyzed using natural language processing [...] Read more.
Social media has become a useful instrument and forum for expressing worries about various difficulties and day-to-day concerns. The pertinent postings containing people’s complaints about water quality as an additional source of information can be automatically acquired/retrieved and analyzed using natural language processing and machine learning approaches. In this paper, we search social media for a water quality analysis and propose a scalable messaging system for quality-related issues to the subscribers. We classify the WaterQualityTweets dataset, our newly collected collection, in two phases. In the first phase, tweets are classified into two classes (water quality-related or not). In the second phase, water quality-related issues are classified into four classes (color, illness, odor/taste, and unusual state). The best performance results are BERT and CNN, respectively, for binary and multi-class classification. Also, these issues are sent to different subscribers via a topic-based system with their location and timing information. Depending on the topics that online users are interested in, some information spreads faster than others. In our dataset, we also predict the information diffusion to understand water quality issues’ spreading. The time and effort required for manual comments obtained through crowd-sourcing techniques will significantly decline as a result of this automatic analysis of water quality issues. Full article
(This article belongs to the Special Issue Smart Technologies for Urban Water Systems)
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23 pages, 1390 KiB  
Article
Impact of Hydraulic Variable Conditions in the Solution of Pumping Station Design through Sensitivity Analysis
Water 2023, 15(17), 3067; https://doi.org/10.3390/w15173067 - 27 Aug 2023
Viewed by 748
Abstract
A proper pumping station (PS) design should consider multiple criteria, such as technical, economic, and environmental aspects. The analytic hierarchy process (AHP) method can be applied for multi-criteria analysis in this type of engineering design, and it is based on the judgment of [...] Read more.
A proper pumping station (PS) design should consider multiple criteria, such as technical, economic, and environmental aspects. The analytic hierarchy process (AHP) method can be applied for multi-criteria analysis in this type of engineering design, and it is based on the judgment of a group of experts for the criteria considered. On the other hand, the most common method for PS design is one based solely on economic aspects or life cycle cost (LCC). This paper presents a sensitivity analysis of the impact of the hydraulic conditions of a water distribution network (WDN) on the ultimate solution in two PS design approaches. The first approach was the classic method based on LCC minimization and the second approach was based on multi-criteria analysis by means of AHP accounting for technical, economic, and environmental aspects. In this way, the effects of different meaningful variables for PS design, such as the mean demand, parameters of the setpoint curve, electric tariffs, and interest rates, were evaluated to determine the robustness of the PS solutions obtained. The obtained results of the sensitivity analysis in the case study demonstrated that the PS design based on multiple criteria decision analysis was more reliable and robust than the classic PS design against variations that can occur in a WDN, especially in the mean flow, setpoint curve, and electric tariff. The variations in these parameters of the WDN did not impact the ultimate solutions of the PS design approaches when within the tolerance ranges, but these ranges were wider in the second approach to PS design than in the first approach. Full article
(This article belongs to the Special Issue Smart Technologies for Urban Water Systems)
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13 pages, 2284 KiB  
Article
Assessment of ERA5-Land Data in Medium-Term Drinking Water Demand Modelling with Deep Learning
Water 2023, 15(8), 1495; https://doi.org/10.3390/w15081495 - 11 Apr 2023
Cited by 1 | Viewed by 1448
Abstract
Drinking water demand modelling and forecasting is a crucial task for sustainable management and planning of water supply systems. Despite many short-term investigations, the medium-term problem needs better exploration, particularly the analysis and assessment of meteorological data for forecasting drinking water demand. This [...] Read more.
Drinking water demand modelling and forecasting is a crucial task for sustainable management and planning of water supply systems. Despite many short-term investigations, the medium-term problem needs better exploration, particularly the analysis and assessment of meteorological data for forecasting drinking water demand. This work proposes to analyse the suitability of ERA5-Land reanalysis data as weather input in water demand modelling. A multivariate deep learning model based on the long short-term memory architecture is used in this study over a prediction horizon ranging from seven days to two months. The performance of the model, fed by ground station data and ERA5-Land data, is compared and analysed. Close-to-operative forecasting is then presented using observed data for training and ERA5-Land dataset for testing. The results highlight the reliability of the proposed architecture fed by ERA5-Land data for different time horizons. In particular, the ERA5-Land shows promising performance as input of the multivariate machine learning forecasting model, although some meteorological biases are present, which can be improved, especially in close-to-operative application with bias correction techniques. The proposed study leads to practical implications in the use of regional climate model outputs to support drinking water forecasting for sustainable and efficient management of water distribution systems. Full article
(This article belongs to the Special Issue Smart Technologies for Urban Water Systems)
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23 pages, 6208 KiB  
Article
Water Requirement in North China from Grey Point Prediction and Grey Interval Prediction
Water 2023, 15(8), 1453; https://doi.org/10.3390/w15081453 - 07 Apr 2023
Cited by 1 | Viewed by 1222
Abstract
Since the implementation of the sustainable development strategy, China has made great efforts to save water resources. Therefore, effective prediction and analysis of regional water consumption are very important for the regional economy. In order to forecast the water requirement of the five [...] Read more.
Since the implementation of the sustainable development strategy, China has made great efforts to save water resources. Therefore, effective prediction and analysis of regional water consumption are very important for the regional economy. In order to forecast the water requirement of the five provinces in North China, the DGMC(1,2) model is proposed to predict the point value of water requirement by considering the three industries and the population. The results turn out that DGMC(1,2) model is an efficient way of predicting water requirements. In addition, the interval value of water requirement is predicted by the establishment of the interval DGMC(1,2) model. According to the prediction results, the variation trend of water requirement in each region is analyzed in detail, and the corresponding suggestions are put forward. The results can have practical value and be used for policy-making. Full article
(This article belongs to the Special Issue Smart Technologies for Urban Water Systems)
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22 pages, 6673 KiB  
Article
Smart Water Grids and Digital Twin for the Management of System Efficiency in Water Distribution Networks
Water 2023, 15(6), 1129; https://doi.org/10.3390/w15061129 - 15 Mar 2023
Cited by 7 | Viewed by 3761
Abstract
One of the main factors contributing to water scarcity is water loss in water distribution systems, which mainly arises from a lack of adequate knowledge in the design process, optimization of water availability, and poor maintenance/management of the system. Thus, from the perspective [...] Read more.
One of the main factors contributing to water scarcity is water loss in water distribution systems, which mainly arises from a lack of adequate knowledge in the design process, optimization of water availability, and poor maintenance/management of the system. Thus, from the perspective of sustainable and integrated management of water resources, it is essential to enhance system efficiency by monitoring existing system elements and enhancing network maintenance/management practices. The current study establishes a smart water grid (SWG) with a digital twin (DT) for a water infrastructure to improve monitoring, management, and system efficiency. Such a tool allows live monitoring of system components, which can analyze different scenarios and variables, such as pressures, operating devices, regulation of different valves, and head-loss factors. The current study explores a case study in which local constraints amplify significant water losses. It develops and examines the DT model’s application in the Gaula water distribution network (WDN) in Madeira Island, Portugal. The developed methodology resulted in a significant potential reduction in real water losses, which presented a huge value of 434,273 m3 (~80%) and significantly improved system efficiency. The result shows a meaningful economic benefit, with savings of about EUR 165k in water loss volume with limiting pressures above the regulatory maximum of 60 m w.c. after the district metered area (DMA) sectorization and the requalification of the network. Hence, only 40% of the total annual volume, concerning the status quo situation, is necessary to supply the demand. The infrastructure leakage index measures the existing real losses and the reduction potential, reaching a value of 21.15, much higher than the recommended value of 4, revealing the great potential for improving the system efficiency using the proposed methodology. Full article
(This article belongs to the Special Issue Smart Technologies for Urban Water Systems)
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13 pages, 2025 KiB  
Article
Computational Tools for Supporting the Operation and Management of Water Distribution Systems towards Digital Transformation
Water 2023, 15(3), 553; https://doi.org/10.3390/w15030553 - 31 Jan 2023
Cited by 2 | Viewed by 1924
Abstract
This paper presents a set of computational tools specially developed for supporting the operation and management of water distribution systems towards digital transformation of water services. These tools were developed in the scope of two R&D projects carried out in Portugal, DECIdE and [...] Read more.
This paper presents a set of computational tools specially developed for supporting the operation and management of water distribution systems towards digital transformation of water services. These tools were developed in the scope of two R&D projects carried out in Portugal, DECIdE and WISDom, during 2018–2022. The DECIdE project focused on the development of tools for importing cadastral and operational data, as well as on the three operational tools for supporting the performance assessment: the first allows the calculation of different key performance indicators, both at a global and sectorial level, which is an annual requirement of the water regulator, and the other two allow the calculation of the water and the energy balances and a set of complementary indices. The WISDom project aimed at the implementation of applications that directly address specific water utility needs, namely, the flow rate data processing, the optimal location of pressure sensors, the identification of critical areas in the distribution network for pipe burst location, and the prioritization of pipes for rehabilitation. Implemented tools are useful to support water utilities in the daily operation and management of their systems, being a step forward towards digital transformation of the water sector. Full article
(This article belongs to the Special Issue Smart Technologies for Urban Water Systems)
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16 pages, 5734 KiB  
Article
An Investigation on the Effect of Leakages on the Water Quality Parameters in Distribution Networks
Water 2023, 15(2), 324; https://doi.org/10.3390/w15020324 - 12 Jan 2023
Cited by 4 | Viewed by 2141
Abstract
Leakages in distribution networks reach more than 30% of the water supplied, entailing important risks for the water infrastructure with water contamination issues. Therefore, it is necessary to develop new methods to mitigate the amount of water wastes. This study proposes to seek [...] Read more.
Leakages in distribution networks reach more than 30% of the water supplied, entailing important risks for the water infrastructure with water contamination issues. Therefore, it is necessary to develop new methods to mitigate the amount of water wastes. This study proposes to seek new sources of information that can help for a more sustainable water use. Hence, an analysis of the network is presented, showing the hydraulic behavior during leaks occurrence, placing emphasis on how these events affect and modify water quality parameters, such as water age and chlorine concentration. The study enhances that water quality data can be an effective source of information in the case of leaks, being a possible source of information for future detection systems. In addition, this study proposes to use graph theory on the water network. The results highlight how an analysis of the shortest path between the leak location and the reservoir could provide meaningful information for future detection systems. Full article
(This article belongs to the Special Issue Smart Technologies for Urban Water Systems)
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