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Optimal Design, Operation, and Management for Sustainable Water Distribution Systems

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (26 March 2023) | Viewed by 13833

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Special Issue Editors

Dr. Do Guen Yoo

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Guest Editor

Department of Civil Engineering, The University of Suwon, Bongdam-eup, Hwaseong-si 18323, Gyeonggi-do, Republic of Korea
Interests: resilience-based design and management of water distribution networks; optimization; meta-heuristics algorithms; advanced metering infrastructures; smart water techniques
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Dr. Seungyub Lee

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Guest Editor

Department of Civil and Environmental Engineering, Hannam University, Daejeon 34430, Republic of Korea
Interests: urban water infrastructure; water distribution system analysis; sustainability; resilience; aging infrastructure; optimization; asset management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The water distribution system is one of the most critical infrastructures in sustaining our communities as it ensures the necessary supplies for daily water usage. However, the water distribution system has been undergoing various problems including but not limited to climate change, population growth, resource depletion, aging infrastructure, pipe breakage, and leakage. All these problems jeopardizing the performance of the water distribution system. Hence, it is important to seek a way to secure (or sustain) the performance of a water distribution system.

There is a general agreement of the definition of sustainability of the water distribution system, but it is often difficult to clearly reflect it in the planning and management phase. Over recent decades, several studies have referred to the sustainability of water distribution systems and solved emerging issues of the water distribution system in the limited perspective of sustainability (e.g., cost-effectiveness, environmental impacts, reliability, resilience, etc.). As the sustainability of the water distribution system is the sustainability of our communities, it is now important to rethink the sustainability and sustainable development of the water distribution system.

The goal of the Special Issue is to focus on emerging topics of sustainable development of the water distribution systems by its sustainable design, operation, and management. The main priorities of the research are as follows:

Defining sustainability of WDS
Identification of relationship among sustainability components
Innovative strategies for sustainable development of WDS
Strategies for securing sustainability of WDS under a mega-disaster
Application of optimization algorithms and artificial intelligence-based techniques to predict long-term performance of WDS
Comprehensive sustainability analysis of WDS
Solving WDS aging issues
Long term adaptive operation considering climate change and population growth
System dynamics modelling of WDS
Carbon neutral techniques of WDS

Combining advanced technology for sustainable operation of WDS

Prof. Dr. Do Guen Yoo
Prof. Dr. Seungyub Lee
Guest Editors

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Keywords

Water distribution system analysis
sustainable development
planning and management
optimization
artificial intelligence
drinking water infrastructure
asset management
disaster management
carbon neutral

Published Papers (8 papers)

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Research

27 pages, 1700 KiB

Open AccessArticle

Development of Vulnerability Evaluation Technology for Environmental Facilities Focused on the Water Treatment Systems in South Korea

by Young Hwan Choi, Do Guen Yoo, Pill Jae Kwak and Younghan Yoon

Sustainability 2023, 15(13), 10257; https://0-doi-org.brum.beds.ac.uk/10.3390/su151310257 - 28 Jun 2023

Viewed by 748

Abstract

This study developed a vulnerability evaluation framework for earthquake and flood disasters targeting water treatment facilities. The vulnerability evaluation framework of a water treatment facility determines vulnerability evaluation factors such as exposure, sensitivity, and adaptive capacity in consideration of the characteristics of an environmental facility. At this time, vulnerability evaluation items are derived in consideration of topography, natural environment, hydraulic, structural, and non-structural characteristics by analyzing the facility. The vulnerability evaluation items consist of 24 for earthquake disasters, and for the flood disaster derived 20 indicators. Then the final vulnerability of disaster is determined by considering the impact of each item. To verify the vulnerability evaluation framework proposed in this study, the technology was applied to a real water treatment facility in Korea. The proposed technique would be able to make a plan to prevent natural disasters damage and minimize such damage to the environmental facility. Full article

(This article belongs to the Special Issue Optimal Design, Operation, and Management for Sustainable Water Distribution Systems)

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18 pages, 4162 KiB

Open AccessArticle

Comparative Study on Strategies for the Division of Earthquake-Proof Strengthening Segments to Reinforce the Reliability of Water Supply Systems

by Chan-Wook Lee and Do-Guen Yoo

Sustainability 2023, 15(8), 6837; https://0-doi-org.brum.beds.ac.uk/10.3390/su15086837 - 18 Apr 2023

Viewed by 765

Abstract

It is very important to secure the sustainability of physical and non-physical social infrastructure facilities in the event of a disaster. The water supply network is particularly vulnerable to seismic damage, and so physical earthquake resistance is very necessary to adapt to or withstand disaster situations. This study evaluated various strategic methods to improve the reliability of water distribution network systems in the event of an earthquake disaster with a focus on structural earthquake-proofing methods for pipelines. For this purpose, three major flow-, diameter- and connection-hierarchy-based earthquake proofing strategies are proposed. We quantified the extent to which earthquake reliability improved after the strengthening of the earthquake-proofing of the pipeline segments, which had been divided based on the proposed strategies. The proposed methodology of dividing the pipeline segments for earthquake-proof strengthening was applied to the water supply system of the Republic of Korea and analyzed thereafter. As a result, it was confirmed that the associated costs and the extent of the improvement in the reliability of earthquake proofing for each strategy and scenario need to be precisely analyzed. Thus, it is necessary to execute strategic earthquake proofing of pipelines with medium size diameters and which occupy most of the length of a mainline, in order to simultaneously satisfy the reliability and cost efficiency of the relevant water supply. However, additional earthquake proofing for segments of a higher level of flowrate is required because a marked drop in overall reliability is caused if they are damaged. In addition, because the effect of an increase in reliability in comparison with the costs incurred is insignificant in the case of some low demand and small-diameter pipeline segments, it is reasonable to exclude earthquake resistance strategies for these sections. The proposed study results—determining the level of importance of each resistance method—can be utilized to make a combined plan for optimal earthquake-proofing strategies. Full article

(This article belongs to the Special Issue Optimal Design, Operation, and Management for Sustainable Water Distribution Systems)

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18 pages, 7533 KiB

Open AccessArticle

Development of the Methodology for Pipe Burst Detection in Multi-Regional Water Supply Networks Using Sensor Network Maps and Deep Neural Networks

by Hyeong-Suk Kim, Dooyong Choi, Do-Guen Yoo and Kyoung-Pil Kim

Sustainability 2022, 14(22), 15104; https://0-doi-org.brum.beds.ac.uk/10.3390/su142215104 - 15 Nov 2022

Cited by 2 | Viewed by 1508

Abstract

Multi-regional waterworks are large-scale facilities for supplying tap water to the public and industrial parks, and interruptions in the water supply due to leaks result in massive social and economic damages. Accordingly, real-time, around-the-clock accident monitoring is necessary to minimize secondary damage. In the present study, a section of a large-scale waterworks transmission mains system with frequent changes in its physical boundaries was defined for sensor network map-based deep learning input and output. A deep neural network (DNN)-based pressure prediction model, able to detect pipe burst accidents in real-time using short-term data collected over periods within 1 month, was developed. A sensor network map refers to a sensor-based hierarchy diagram, which is expressed using a hydraulically divided area. A hydraulically independent area can be determined using known value information (e.g., the known flow, pressure, and total head) in a complex water supply system. The input data used for the deep learning model training were: the water levels measured at 1 min intervals, flow rates, ambient pressure, pump operation state, and electric valve opening data. To verify the developed methodology, two sets of real-world data from past burst accidents in different multi-regional waterworks systems were used. The results showed that the difference between the pressure as measured by pressure meters and an estimated pressure was extremely small before an accident, and that the difference would reach a maximum at the time point when an accident occurs. It was confirmed that an approximate estimation of an accident occurrence and accident location could be estimated based on predicted pressure meter data. The developed methodology predicts a mutual influence between pressure meters and, therefore, has the advantage of not requiring past data covering long time periods. The proposed methodology can be applied immediately and used in currently operational large-scale water transmission main systems. Full article

(This article belongs to the Special Issue Optimal Design, Operation, and Management for Sustainable Water Distribution Systems)

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19 pages, 7318 KiB

Open AccessArticle

Hyperparameter Sensitivity Analysis of Deep Learning-Based Pipe Burst Detection Model for Multiregional Water Supply Networks

by Hyeong-Suk Kim, Dooyong Choi, Do-Guen Yoo and Kyoung-Pil Kim

Sustainability 2022, 14(21), 13788; https://0-doi-org.brum.beds.ac.uk/10.3390/su142113788 - 24 Oct 2022

Cited by 4 | Viewed by 1666

Abstract

In a deep learning model, the effect of the model may vary depending on the setting of the hyperparameters. Despite the importance of such hyperparameter determination, most previous studies related to burst detection models of the water supply pipe network used hyperparameters applied in other fields as-is or made a trial-and-error setting based on experience, which is a limitation. In this paper, a study was conducted on the deep learning hyperparameter determination of a deep neural network (DNN)-based real-time detection model of pipe burst accidents. The pipe burst model predicted water pressure by using operation data in units of 1 min, and the data period applied for the model training was less than 1 month (1, 2, and 3 weeks) in order to consider frequent changes in the system. A sensitivity analysis was first performed on the type of activation function and the period of the learning data, which may have different effects depending on the characteristics of the target problem. The number of hidden layers related to the network structure and the number of neurons in each hidden layer were set as hyperparameters for additional sensitivity analysis. The sensitivity analysis results were derived and compared using four quantified prediction error indicators. In addition, the model running time was analyzed to evaluate the practical applicability of the development model. From the results, it was confirmed that excellent effects could be expected if using a rectifier function as the activation function, 144 nodes in the hidden layer, which is eight times the number of nodes in the input layer, and four hidden layers. Additionally, by analyzing the appropriate period of training data required for model pressure prediction through prediction error and driving time, it was confirmed that it was most appropriate to use the data of two weeks. By applying the hyperparameter values determined through detailed sensitivity analysis and by applying the data of one week including actual burst accidents to the built-up pressure prediction model, the accident detection and predictive performance of the model were verified. The rational determination of the period of input factors for the optimal hyperparameter setting and model building, as in this study, is very necessary and very important as it can serve to ensure the continuity of the operation effects of the deep learning model. Full article

(This article belongs to the Special Issue Optimal Design, Operation, and Management for Sustainable Water Distribution Systems)

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19 pages, 3983 KiB

Open AccessArticle

Evaluation of Priority Control District Metered Area for Water Distribution Networks Using Water Quality-Related Big Data

by Taehyeon Kim, Yoojin Oh, Jayong Koo and Doguen Yoo

Sustainability 2022, 14(12), 7282; https://0-doi-org.brum.beds.ac.uk/10.3390/su14127282 - 14 Jun 2022

Viewed by 1487

Abstract

Partitioning methodologies such as district metered areas (DMAs) are being applied to the stable maintenance of water distribution network systems in normal conditions such as daily operation and abnormal conditions such as water quality and leakage accidents. However, management and evaluation through the use of existing DMAs generally only have the primary goal of stable water quantity and pressure management. Therefore, the methodology can be limited to achieving the direct effects of water quality parameters such as decreased water age, proper management of residual chlorine, and decreased water quality complaints. This study uses a methodology for determining and prioritizing water quality-oriented Priority Control District Metered Areas (PCDMAs) for stable water quality management to respond to the recent large-scale rusty (red) water crisis in Korea. First, 4 evaluation criteria and 11 evaluation indicators were derived using various water quality-related structured data (water quality measurement data, pipeline data, etc.) and unstructured data (water quality complaints, etc.) based on the Geographic Information System (GIS). A comprehensive prioritization assessment was carried out with multi-criteria decision-making methods based on the analytic hierarchy process. As a result, particular indicators of complaint of water quality and the existence of vulnerable facilities (hospitals, school, etc.) were analyzed as the top five priorities, and it was shown that to be important criteria in determining water quality-oriented PCMDAs. Finally, the proposed methodology was applied to the B metropolitan city of the Republic of Korea, and the evaluation results of all the districts were derived and analyzed. The study shows that the data-based water distribution network PCDMAs selection methodology can be used as a decision-making tool to improve the accuracy and reliability of the operation and management (O&M) of the water distribution operator’s water distribution network. In future research, it will be necessary to evaluate PDDMA with detailed data related to the pipe deterioration (buried environment, the condition of internal/external of the pipe, etc.), which had a significant threshold due to data limitations. And it would be possible to make a real-time evaluation of PCDMA with the real-time water quality test data. Full article

(This article belongs to the Special Issue Optimal Design, Operation, and Management for Sustainable Water Distribution Systems)

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16 pages, 3067 KiB

Open AccessArticle

Strategy to Enhance Emergency Interconnected Operation of Water Distribution System

by Hwandon Jun, Arin Gim, Donghwi Jung and Seungyub Lee

Sustainability 2022, 14(10), 5804; https://0-doi-org.brum.beds.ac.uk/10.3390/su14105804 - 11 May 2022

Cited by 2 | Viewed by 1359

Abstract

This study identified the causes of insufficient emergency interconnected operation (EIO) performance, such as pressure-related problems and connection problems caused by elevation differences between blocks, the characteristics and locations of emergency interconnection pipes (EIPs), and pumps. Then, it tested four strategies to improve the EIO performance, including increasing the EIP diameter or installing additional EIPs, pressure reducing valves (PRVs), or pumps. The advanced pressure-driven analysis model was applied to quantify the EIO performance improvement achieved using these strategies. Further, these strategies were tested in a real water distribution system. To solve the low-pressure problem, the EIP diameter was increased and an additional pump was installed; the former did not significantly improve, whereas the latter improved supply by 20–30%. To solve the high-pressure problem, PRVs were installed to maintain the EIO performance effectively. To solve connection problems, new EIPs were installed. Although this improved the supply performance, the installation of pumps was recommended to overcome elevation differences. The proposed strategies should contribute to the allocation of facilities such as EIPs, pumps, and PRVs for realizing effective EIO. Full article

(This article belongs to the Special Issue Optimal Design, Operation, and Management for Sustainable Water Distribution Systems)

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15 pages, 2394 KiB

Open AccessArticle

Development of Leakage Detection Model and Its Application for Water Distribution Networks Using RNN-LSTM

by Chan-Wook Lee and Do-Guen Yoo

Sustainability 2021, 13(16), 9262; https://0-doi-org.brum.beds.ac.uk/10.3390/su13169262 - 18 Aug 2021

Cited by 19 | Viewed by 3404

Abstract

With the advent of the 4th Industrial Revolution, advanced measurement infrastructure and utilization technologies are being noticeably introduced into the water supply system to store and utilize measurement data. From this perspective, the leak detection technology in water supply networks is becoming increasingly vital to sustainable water resource management and the clean water supply worldwide. In particular, leakage detection of buried pipelines is rated as a very challenging research topic given the current level of technology. However, leakage in buried underground pipelines is rated as a very challenging research topic given the current level of technology. Therefore, a data-driven leak detection model was developed through this study using deep learning technology based on inflow meter data. Multiple threshold-based models were applied to reduce the RNN-LSTM (Recurrent Neural Networks–Long Short-Term Memory models) deep learning and false prediction range, which is programmed in conjunction with the Python language and Google Colaboratory (a big data analysis tool). The developed model consists of flow pattern shape extraction, RNN-LSTM-based flow prediction, and threshold setting modules. The developed model was applied to the actual leakage accident data, followed by the performance evaluation. As a result, the leak was recognized at most points immediately after the accident. The performance of leak detection was evaluated by a Confusion matrix and showed more than 90% accuracy at all points except singularities. Therefore, the developed model can be used as a critical software technology to proactively identify various at present with smart water infrastructure being introduced. In addition, this model is highly scalable as it can consider various operational situations based on the expert system, and it can also efficiently reflect the results of pipe network analysis across different scenarios. Full article

(This article belongs to the Special Issue Optimal Design, Operation, and Management for Sustainable Water Distribution Systems)

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16 pages, 5204 KiB

Open AccessArticle

Multiple Leak Detection in Water Distribution Networks Following Seismic Damage

by Jeongwook Choi, Gimoon Jeong and Doosun Kang

Sustainability 2021, 13(15), 8306; https://0-doi-org.brum.beds.ac.uk/10.3390/su13158306 - 26 Jul 2021

Cited by 4 | Viewed by 2027

Abstract

Water pipe leaks due to seismic damage are more difficult to detect than bursts, and such leaks, if not repaired in a timely manner, can eventually reduce supply pressure and generate both pollutant penetration risks and economic losses. Therefore, leaks must be promptly identified, and damaged pipes must be replaced or repaired. Leak-detection using equipment in the field is accurate; however, it is a considerably labor-intensive process that necessitates expensive equipment. Therefore, indirect leak detection methods applicable before fieldwork are necessary. In this study, a computer-based, multiple-leak-detection model is developed. The proposed technique uses observational data, such as the pressure and flow rate, in conjunction with an optimization method and hydraulic analysis simulations, to improve detection efficiency (DE) for multiple leaks in the field. A novel approach is proposed, i.e., use of a cascade and iteration search algorithms to effectively detect multiple leaks (with the unknown locations, quantities, and sizes encountered in real-world situations) due to large-scale disasters, such as earthquakes. This method is verified through application to small block-scale water distribution networks (WDNs), and the DE is analyzed. The proposed detection model can be used for efficient leak detection and the repair of WDNs following earthquakes. Full article

(This article belongs to the Special Issue Optimal Design, Operation, and Management for Sustainable Water Distribution Systems)

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