Management and Optimization of Urban Water Networks

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 3612

Special Issue Editor


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Guest Editor
College of Environmental Science and Engineering, Tongji University, Shanghai, China
Interests: hydrologic and water quality modeling; watershed hydrology; machine learning model; environmental modeling with GIS; stormwater management; urban non-point source pollution control
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Special Issue Information

Dear Colleagues,

Sustainable water systems, which have benefits in terms of both cost and the environment, are now facilitating the management and optimization of urban water networks, which enables water systems to evolve into adaptive and resilient systems by maximizing the performance of existing water infrastructures. This goal depends on the support of intelligent control theories, as well as state-of-the art control approaches or technologies, to achieve low-cost management. Currently, the knowledge gaps and challenges may include (1) large-scale non-linear and multi-objective optimization models (machine learning models or deterministic models); (2) the system-wide management and optimization of urban water networks (such as integrated framework of drainage network–wastewater treatment plant–urban river, water supply network–drinking water plant); (3) the incorporation of on-line sensors with mathematical models to perform the real-time management of urban water networks; and (4) real practice to demonstrate the management and optimization of urban water networks. The aim of this Special Issue is to collect and share the innovative ideas and results on such topics. We are pleased to invite authors to publish original research articles, review articles, and short communications on relevant topics.

Prof. Dr. Hailong Yin
Guest Editor

Manuscript Submission Information

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Keywords

  • urban drainage network
  • urban water supply network
  • drinking water plant
  • wastewater treatment plant
  • urban river network
  • machine-learning-based optimization
  • deterministic-model-based optimization
  • cost-effective management and optimization
  • multi-objective coordinated optimization
  • smart water system control and management

Published Papers (3 papers)

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Research

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18 pages, 4620 KiB  
Article
A Computational Tool to Track Sewage Flow Discharge into Rivers Based on Coupled HEC-RAS and DREAM
by Junbo Wen, Mengdie Ju, Zichen Jia, Lei Su, Shanshan Wu, Yuting Su, Wenxiao Liufu and Hailong Yin
Water 2024, 16(1), 51; https://0-doi-org.brum.beds.ac.uk/10.3390/w16010051 - 22 Dec 2023
Viewed by 889
Abstract
Worldwide abatement of untreated sewage discharge into surface water is a challenging task. Sewage discharging into surface waters has a detrimental impact on water quality. This paper presents a MATLAB (R2018b) framework designed to identify sewage flow discharges into rivers from an inverse [...] Read more.
Worldwide abatement of untreated sewage discharge into surface water is a challenging task. Sewage discharging into surface waters has a detrimental impact on water quality. This paper presents a MATLAB (R2018b) framework designed to identify sewage flow discharges into rivers from an inverse problem-solving perspective. The computational tool integrates a hydrodynamic model using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS 5.0.0) and an open-source toolbox for Differential Evolution Adaptive Metropolis (DREAM) as the inverse problem method. The proposed framework can effectively infer discharge sources in scenarios of highly transient flow based on hydraulic data at pre-set monitoring sites. To validate its capabilities, one hypothetical case and two real cases of sewage flow discharges entering a river were used to test the developed modeling framework. The results based on three performance metrics showed that this mathematical tool can be extended to simulate complex hydrodynamic flow patterns. This accomplishment underscores its potential as a valuable asset for environmental monitoring and water quality restoration efforts. Full article
(This article belongs to the Special Issue Management and Optimization of Urban Water Networks)
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12 pages, 2306 KiB  
Article
Optimized Sensor Placement of Water Supply Network Based on Multi-Objective White Whale Optimization Algorithm
by Yihong Guan, Mou Lv, Shuyan Li, Yanbo Su and Shen Dong
Water 2023, 15(15), 2677; https://0-doi-org.brum.beds.ac.uk/10.3390/w15152677 - 25 Jul 2023
Cited by 2 | Viewed by 856
Abstract
The optimization of sensor locations in water distribution networks has been extensively studied. Previous studies of highly sensitive nodes are usually distributed in a certain area, which leads to redundant information in the sensor network. This is because these studies do not consider [...] Read more.
The optimization of sensor locations in water distribution networks has been extensively studied. Previous studies of highly sensitive nodes are usually distributed in a certain area, which leads to redundant information in the sensor network. This is because these studies do not consider that the impact is different when a leak occurs in different nodes. In this study, sensitivity functions of different nodes were obtained according to the influence of the leakage of each node on the water distribution network. Combined with the water pressure correlation and water pressure sensitivity between nodes, the monitoring range of monitoring points and the water demand of covering nodes of monitoring points were taken as objective functions to build an optimal layout model. Taking a pipeline network in Qingdao as an example, the model was solved by using multi-objective White Whale Optimization and NSGA-II. By comparing the operation results of the four cases, it was found that the monitoring points found using multi-objective White Whale Optimization show better searching ability in terms of the sensitivity functions of different nodes. Full article
(This article belongs to the Special Issue Management and Optimization of Urban Water Networks)
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Review

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17 pages, 2450 KiB  
Review
The Scientific Landscape of Smart Water Meters: A Comprehensive Review
by Antonio Jesús Zapata-Sierra, Esther Salmerón-Manzano, Alfredo Alcayde, María Lourdes Zapata-Castillo and Francisco Manzano-Agugliaro
Water 2024, 16(1), 113; https://0-doi-org.brum.beds.ac.uk/10.3390/w16010113 - 28 Dec 2023
Viewed by 1343
Abstract
This review underscores the escalating global research trend in this field since 2000. The primary scientific disciplines contributing to extensive research on smart water meters are engineering, computer science, and energy. In terms of countries, the analysis reveals that the United States, India, [...] Read more.
This review underscores the escalating global research trend in this field since 2000. The primary scientific disciplines contributing to extensive research on smart water meters are engineering, computer science, and energy. In terms of countries, the analysis reveals that the United States, India, and China exhibit the highest scientific production. Concerning affiliations, prominent contributors include Griffith University, Politecnico di Milano, and the Università degli Studi di Salerno. Regarding worldwide research trends, an examination of distinct clusters defined by their principal keywords was conducted. The following clusters were identified in order of significance based on the number of publications: Urban Water Meters, IoT Connection, Communication and Security, Grid Management, Water Networks, Hot Water, Groundwater Monitoring, and Smart Irrigation. Of particular note is the growing use of machine learning applications, especially in the management of distribution networks. This trend opens up a promising avenue for addressing complex problems in real time. Full article
(This article belongs to the Special Issue Management and Optimization of Urban Water Networks)
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