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Water
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Hydroinformatics and Integrated Urban Water Management
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Hydroinformatics and Integrated Urban Water Management

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

Deadline for manuscript submissions: closed (15 June 2022) | Viewed by 9618

Special Issue Editors

Prof. Dr. Christos Makropoulos

E-Mail Website
Guest Editor
Department of Water Resources and Environmental Engineering, National Technical University of Athens, Athens, Greece
Interests: urban water management; water systems resilience; critical water infrastructure risk and security analysis; uncertainty quantification; multi-objective evolutionary optimization; decision support; long-term policy scenario development and system stress-testing
Special Issues, Collections and Topics in MDPI journals
Dr. Panagiotis Kossieris

E-Mail Website
Guest Editor
Department of Water Resources and Environmental Engineering, National Technical University of Athens, Athens, Greece
Interests: urban water management; hydroinformatics and water analytics; uncertainty quantification and modelling; stochastic modelling and simulation; system analysis and optimization

Special Issue Information

Dear Colleagues,

Urban water systems are characterized by high complexity and are composed of different types of interconnected infrastructures supporting multiple critical services. These systems are continuously stressed by uncertainties in the supply (e.g., climate crisis) and demand (e.g., urbanization, geopolitical changes) side, the inevitable aging of water infrastructures, and the lack of related investments. To address the water-related challenges, smarter hydroinformatics applications, digital services, and tools are continuously being developed and deployed to support the integrated management of urban water systems. Such developments have been substantially fostered by the ever-increasing deployment of information and communication technologies (ICT), advances in computational power, and the continuous expansion of AI/ML solutions in the water sector. The ongoing research activities and solutions are extended to a wide spectrum of interconnected and overlapping fields in the realm of hydroinformatics, which can be broadly categorized into:

  • ICT services which unfold new streams of water-related information, along with information platforms and digital solutions able to process, manage, analyze, and visualize large amounts of data in real-time, such as Digital Twins;
  • Decentralized infrastructures and technologies, along with solutions for their monitoring and remote control, and methodologies for their modeling and simulation;
  • Analytics, including stochastic analysis and simulation methods, optimization tools, artificial intelligence, and machine learning models;
  • Integrated modeling frameworks, capturing the interactions between centralized and distributed infrastructure solutions, the integrations between natural and engineered infrastructure systems and interplay with the users, the integration between the physical and cyber layer of water systems;
  • New forms of interactive and immersive decision making, such as serious games and augmented reality applications;
  • New design concept and strategies, such as resilience, to allow the more realistic evaluation of water systems under stress testing;
  • New data standardization approaches to accelerate the development of integrated smart solutions.

This Special Issue is intended to bring together the latest developments and research efforts on the abovementioned key domains of hydroinformatics applications that focus especially on the integrated management of urban water.

Prof. Dr. Christos Makropoulos
Dr. Panagiotis Kossieris
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • whole cycle urban water models
  • integrated urban water management
  • water analytics
  • digital services
  • resilience
  • cybersecurity
  • asset management
  • decentralised technologies
  • digital twins
  • critical infrastructure security and safety

Published Papers (3 papers)

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Research

15 pages, 3826 KiB  
Article
Automated Customer Complaint Processing for Water Utilities Based on Natural Language Processing—Case Study of a Dutch Water Utility
by Xin Tian, Ina Vertommen, Lydia Tsiami, Peter van Thienen and Sotirios Paraskevopoulos
Water 2022, 14(4), 674; https://0-doi-org.brum.beds.ac.uk/10.3390/w14040674 - 21 Feb 2022
Cited by 4 | Viewed by 3524
Abstract
Most water utilities have to handle a substantial number of customer complaints every year. Traditionally, complaints are handled by skilled staff who know how to identify primary issues, classify complaints, find solutions, and communicate with customers. The effort associated with complaint processing is [...] Read more.
Most water utilities have to handle a substantial number of customer complaints every year. Traditionally, complaints are handled by skilled staff who know how to identify primary issues, classify complaints, find solutions, and communicate with customers. The effort associated with complaint processing is often great, depending on the number of customers served by a water utility. However, the rise of natural language processing (NLP), enabled by deep learning, and especially the use of deep recurrent and convolutional neural networks, has created new opportunities for comprehending and interpreting text complaints. As such, we aim to investigate the value of the use of NLP for processing customer complaints. Through a case study about the Water Utility Groningen in the Netherlands, we demonstrate that NLP can parse language structures and extract intents and sentiments from customer complaints. As a result, this study represents a critical and fundamental step toward fully automating consumer complaint processing for water utilities. Full article
(This article belongs to the Special Issue Hydroinformatics and Integrated Urban Water Management)
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14 pages, 2735 KiB  
Article
Flow Measurements Derived from Camera Footage Using an Open-Source Ecosystem
by Robert Meier, Franz Tscheikner-Gratl, David B. Steffelbauer and Christos Makropoulos
Water 2022, 14(3), 424; https://0-doi-org.brum.beds.ac.uk/10.3390/w14030424 - 29 Jan 2022
Cited by 4 | Viewed by 3404
Abstract
Sensors used for wastewater flow measurements need to be robust and are, consequently, expensive pieces of hardware that must be maintained regularly to function correctly in the hazardous environment of sewers. Remote sensing can remedy these issues, as the lack of direct contact [...] Read more.
Sensors used for wastewater flow measurements need to be robust and are, consequently, expensive pieces of hardware that must be maintained regularly to function correctly in the hazardous environment of sewers. Remote sensing can remedy these issues, as the lack of direct contact between sensor and sewage reduces the hardware demands and need for maintenance. This paper utilizes off-the-shelf cameras and machine learning algorithms to estimate the discharge in open sewer channels. We use convolutional neural networks to extract the water level and surface velocity from camera images directly, without the need for artificial markers in the sewage stream. Under optimal conditions, our method estimates the water level with an accuracy of ±2.48% and the surface velocity with an accuracy of ±2.08% in a laboratory setting—a performance comparable to other state-of-the-art solutions (e.g., in situ measurements). Full article
(This article belongs to the Special Issue Hydroinformatics and Integrated Urban Water Management)
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21 pages, 2939 KiB  
Article
Generic Framework for Downscaling Statistical Quantities at Fine Time-Scales and Its Perspectives towards Cost-Effective Enrichment of Water Demand Records
by Panagiotis Kossieris, Ioannis Tsoukalas, Andreas Efstratiadis and Christos Makropoulos
Water 2021, 13(23), 3429; https://0-doi-org.brum.beds.ac.uk/10.3390/w13233429 - 03 Dec 2021
Cited by 1 | Viewed by 1713
Abstract
The challenging task of generating a synthetic time series at finer temporal scales than the observed data, embeds the reconstruction of a number of essential statistical quantities at the desirable (i.e., lower) scale of interest. This paper introduces a parsimonious and general framework [...] Read more.
The challenging task of generating a synthetic time series at finer temporal scales than the observed data, embeds the reconstruction of a number of essential statistical quantities at the desirable (i.e., lower) scale of interest. This paper introduces a parsimonious and general framework for the downscaling of statistical quantities based solely on available information at coarser time scales. The methodology is based on three key elements: (a) the analysis of statistics’ behaviour across multiple temporal scales; (b) the use of parametric functions to model this behaviour; and (c) the exploitation of extrapolation capabilities of the functions to downscale the associated statistical quantities at finer scales. Herein, we demonstrate the methodology using residential water demand records and focus on the downscaling of the following key quantities: variance, L-variation, L-skewness and probability of zero value (no demand; intermittency), which are typically used to parameterise a stochastic simulation model. Specifically, we downscale the above statistics down to a 1 min scale, assuming two scenarios of initial data resolution, i.e., 5 and 10 min. The evaluation of the methodology on several cases indicates that the four statistics can be well reconstructed. Going one step further, we place the downscaling methodology in a more integrated modelling framework for a cost-effective enhancement of fine-resolution records with synthetic ones, embracing the current limited availability of fine-resolution water demand measurements. Full article
(This article belongs to the Special Issue Hydroinformatics and Integrated Urban Water Management)
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