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Special Issue "Smart Technologies and Water Supply Planning"

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

Deadline for manuscript submissions: 30 September 2019.

Special Issue Editors

Guest Editor
Assoc. Prof. Ashok Sharma

Institute for Sustainable Industries & Liveable Cities and College of Engineering and Science, Victoria University, Ballarat Rd, Footscray VIC 3011, Melbourne, Australia
Website | E-Mail
Phone: +61 3 9919 4519
Interests: urban water; wastewater and stormwater systems; decentralised systems; hydraulic and hydrology; integrated urban water management; sustainability assessment; water resources; water sensitive urban design
Guest Editor
Prof. Ted Gardner

Institute for Sustainable Industries & Liveable Cities, Victoria University, Ballarat Rd, Footscray VIC 3011, Melbourne, Australia
E-Mail
Phone: +61 4 17729181
Interests: decentralised and on-site sewerage systems; integrated urban water management; water sensitive urban design; irrigation systems; wastewater recycling;, system thinking as applied to the Urban Water Cycle, metabolism of ecologically sensitive subdivision (water, energy and nutrient balances), quantitative microbial risk assessment of alternative urban water supplies etc.

Special Issue Information

Dear Colleagues,

Urbanisation, population growth and climate change drive the planning of water supply systems. Urban developments are facing a shortage of fresh water resources, yet somewhat perversely, an increase in their wastewater and stormwater generation; adverse impacts on the ecology of the receiving water environment; aging infrastructure; financial constraints and increase in GHG emissions.

Smart technologies can play an important role in the better planning, design, implementation, operation and maintenance of water supply systems. These technologies may include: application of geospatial technologies including remote sensing; pressure reducing systems to mitigate potable water mains pipe bursts; maintaining demand driven pressure in systems; minimising non-revenue water by timely location of leaks; real time monitoring of systems; application of IoT for water quantity and quality monitoring; use of ITC and control systems; real time water network analysis for system control, management of rainwater storages for reducing peak runoff rates, real time feed back of water consumption and price to customers, alerting customers to hidden (sub surface) water leaks.

The application of smart technologies in also being promoted in water systems for: managing stormwater harvesting systems,  local aquifers to store stormwater (ASR), real time detection of pathogens using chip based DNA technology, critical control points to ensure “out of spec water” does not pass onto the next treatment step, real time sensors & alert systems  for cross connection in dual reticulation systems in suburbs  and on demand UV systems (i.e., LED based) to reduce energy use from “under the sink” domestic installations.

 

Assoc. Prof. Ashok Sharma
Prof. Ted Gardner
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 papers will be 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 monthly 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 1600 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

  • water supply systems
  • smart technologies
  • real time monitoring
  • contro system
  • IoT
  • remote sensing

Published Papers (3 papers)

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Research

Open AccessArticle
Sizing of Domestic Rainwater Harvesting Systems Using Economic Performance Indicators to Support Water Supply Systems
Water 2019, 11(4), 783; https://doi.org/10.3390/w11040783
Received: 20 March 2019 / Revised: 10 April 2019 / Accepted: 11 April 2019 / Published: 15 April 2019
PDF Full-text (3022 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper presents a monitoring-based investigation of rainwater collection systems using economic performance indicators in a group of households with nonconventional end-uses for rainwater that are not traditionally associated with rainwater supply. The monitored data for five household rainwater tank systems were analysed [...] Read more.
This paper presents a monitoring-based investigation of rainwater collection systems using economic performance indicators in a group of households with nonconventional end-uses for rainwater that are not traditionally associated with rainwater supply. The monitored data for five household rainwater tank systems were analysed in two stages. For the first stage, the data was empirically analysed to develop a method to predict effective roof catchment areas. For the second stage, the effective roof catchment areas, together with roof area connection percentages, were analysed against different types of water demands in individual households. The individual systems were investigated for yield capacities, costs and water security using a modified Roof Runoff Harvesting Systems average annual yield model based on daily water balance procedures. The Life Cycle Costing analysis of the systems using the model was based on the Capital Recovery Method by taking into consideration the capital costs as well as ongoing costs for maintenance, replacement and operation of the systems. The analysis established the optimal sizing requirements for the studied rainwater tanks and their corresponding roof area connectivity. Full article
(This article belongs to the Special Issue Smart Technologies and Water Supply Planning)
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Open AccessArticle
Using SCADA to Detect and Locate Bursts in a Long-Distance Water Pipeline
Water 2018, 10(12), 1727; https://doi.org/10.3390/w10121727
Received: 24 October 2018 / Revised: 20 November 2018 / Accepted: 20 November 2018 / Published: 26 November 2018
Cited by 2 | PDF Full-text (4577 KB) | HTML Full-text | XML Full-text
Abstract
Pipe bursting is a serious problem for water supply systems. We propose a two-step burst detection and localization method for a long-distance water transportation pipeline. First, we use the Dempster–Shafer theory, an effective inference method for processing uncertain information, and combine two risk [...] Read more.
Pipe bursting is a serious problem for water supply systems. We propose a two-step burst detection and localization method for a long-distance water transportation pipeline. First, we use the Dempster–Shafer theory, an effective inference method for processing uncertain information, and combine two risk functions to identify a pipe burst. Then we identify the location of the burst point using a hydraulic model. The method is prototyped on a transportation pipeline in Guangzhou, China and tested with one-year historical records. The detection system correctly identified all the bursts and the alarm rate is acceptable for the system inspectors (average: two alarms/month). The burst location is identified within the acceptable limits of accuracy. Full article
(This article belongs to the Special Issue Smart Technologies and Water Supply Planning)
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Open AccessArticle
Enhancing Residential Water End Use Pattern Recognition Accuracy Using Self-Organizing Maps and K-Means Clustering Techniques: Autoflow v3.1
Water 2018, 10(9), 1221; https://doi.org/10.3390/w10091221
Received: 30 July 2018 / Revised: 29 August 2018 / Accepted: 6 September 2018 / Published: 10 September 2018
Cited by 1 | PDF Full-text (1763 KB) | HTML Full-text | XML Full-text
Abstract
The aim of residential water end-use studies is to disaggregate water consumption into different water end-use categories (i.e., shower, toilet, etc.). The authors previously developed a beta application software (i.e., Autoflow v2.1) that provides an intelligent platform to autonomously categorize residential water [...] Read more.
The aim of residential water end-use studies is to disaggregate water consumption into different water end-use categories (i.e., shower, toilet, etc.). The authors previously developed a beta application software (i.e., Autoflow v2.1) that provides an intelligent platform to autonomously categorize residential water consumption data and generate management analysis reports. However, the Autoflow v2.1 software water end use event recognition accuracy achieved was between 75 to 90%, which leaves room for improvement. In the present study, a new module augmented to the existing procedure improved flow disaggregation accuracy, which resulted in Autoflow v3.1. The new module applied self-organizing maps (SOM) and K-means clustering algorithms for undertaking an initial pre-grouping of water end-use events before the existing pattern recognition procedures were applied (i.e., ANN, HMM, etc.) For validation, a dataset consisting of over 100,000 events from 252 homes in Australia were employed to verify accuracy improvements derived from augmenting the new hybrid SOM and K-means algorithm techniques into the existing Autoflow v2.1 software. The water end use event categorization accuracy ranged from 86 to 94.2% for the enhanced model (Autoflow v3.1), which was a 1.7 to 9% improvement on event categorization. Full article
(This article belongs to the Special Issue Smart Technologies and Water Supply Planning)
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