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Application of Machine Learning in Urban Water Management: Recent Advances...
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Application of Machine Learning in Urban Water Management: Recent Advances and Prospects

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

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 8700

Special Issue Editors

Prof. Dr. Kunlun Xin

E-Mail Website
Guest Editor
1. College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
2. Smart Water Joint Innovation RD Center, Tongji University, Shanghai, 200092, China
Interests: water distribution system modelling; optimal operation of water distribution system; burst and leakage detection and control
Dr. Hexiang Yan

E-Mail Website
Guest Editor
1. College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
2. Smart Water Joint Innovation RD Center, Tongji University, Shanghai, 200092, China
Interests: water distribution system modelling; optimal operation of water distribution system; pollution source identification in water distribution system; hydraulic modeling in urban drainage system

Special Issue Information

Dear Colleagues,

Machine learning is an important tool which enjoys widespread usage urban water management. Neural networks, support vector machines, cluster analysis techniques and other methods have been successfully applied in predictions of urban water consumption, detection and location of bursts and leaks, bust risk evaluation of pipes, and identification of contamination accidents. However, on the other hand, the application of these machine learning methods still has great limitations, especially for urban water supply pipe networks and drainage pipe networks with complex structure and operation status. These methods show different degrees of shortcomings in applicability and accuracy, and cannot form all-weather online technical applications. With the continuous development of Internet of Things technology, the data accumulated in urban water management have been greatly expanded in terms of quantity and dimension. These expansions also provide the possibility for the application of new algorithms such as deep learning in urban water management, so as to achieve the more intelligent and refined management of urban water supply and drainage systems. The objective of this Special Issue is to compile the latest advances in the application of machine learning in urban water management, including new research methods, successful application cases, reviews and analyses on this topic, etc., so as to provide a reference for researchers and engineers in this field.

Prof. Dr. Kunlun Xin
Dr. Hexiang Yan
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

  • water distribution system
  • drainage system
  • hydraulic modelling
  • advanced metering infrastructures
  • pipe burst detection
  • real time scheduling
  • machine learning techniques
  • state estimation

Published Papers (5 papers)

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Research

23 pages, 20236 KiB  
Article
Urban Water Demand Prediction Based on Attention Mechanism Graph Convolutional Network-Long Short-Term Memory
by Chunjing Liu, Zhen Liu, Jia Yuan, Dong Wang and Xin Liu
Water 2024, 16(6), 831; https://doi.org/10.3390/w16060831 - 13 Mar 2024
Viewed by 800
Abstract
Predicting short-term urban water demand is essential for water resource management and directly impacts urban water resource planning and supply–demand balance. As numerous factors impact the prediction of short-term urban water demand and present complex nonlinear dynamic characteristics, the current water demand prediction [...] Read more.
Predicting short-term urban water demand is essential for water resource management and directly impacts urban water resource planning and supply–demand balance. As numerous factors impact the prediction of short-term urban water demand and present complex nonlinear dynamic characteristics, the current water demand prediction methods mainly focus on the time dimension characteristics of the variables, while ignoring the potential influence of spatial characteristics on the temporal characteristics of the variables. This leads to low prediction accuracy. To address this problem, a short-term urban water demand prediction model which integrates both spatial and temporal characteristics is proposed in this paper. Firstly, anomaly detection and correction are conducted using the Prophet model. Secondly, the maximum information coefficient (MIC) is used to construct an adjacency matrix among variables, which is combined with a graph convolutional neural network (GCN) to extract spatial characteristics among variables, while a multi-head attention mechanism is applied to enhance key features related to water use data, reducing the influence of unnecessary factors. Finally, the prediction of short-term urban water demand is made through a three-layer long short-term memory (LSTM) network. Compared with existing prediction models, the hybrid model proposed in this study reduces the average absolute percentage error by 1.868–2.718%, showing better prediction accuracy and prediction effectiveness. This study can assist cities in rationally allocating water resources and lay a foundation for future research. Full article
(This article belongs to the Special Issue Application of Machine Learning in Urban Water Management: Recent Advances and Prospects)
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26 pages, 1335 KiB  
Article
Two-Leak Isolation in Water Distribution Networks Based on k-NN and Linear Discriminant Classifiers
by Carlos Andrés Rodríguez-Argote, Ofelia Begovich-Mendoza, Adrián Navarro-Díaz, Ildeberto Santos-Ruiz, Vicenç Puig and Jorge Alejandro Delgado-Aguiñaga
Water 2023, 15(17), 3090; https://doi.org/10.3390/w15173090 - 29 Aug 2023
Cited by 1 | Viewed by 1451
Abstract
In this paper, the two-simultaneous-leak isolation problem in water distribution networks is addressed. This methodology relies on optimal sensor placement together with a leak location strategy using two well-known classifiers: k-NN and discriminant analysis. First, zone segmentation of the water distribution network is [...] Read more.
In this paper, the two-simultaneous-leak isolation problem in water distribution networks is addressed. This methodology relies on optimal sensor placement together with a leak location strategy using two well-known classifiers: k-NN and discriminant analysis. First, zone segmentation of the water distribution network is proposed, aiming to reduce the computational cost that involves all possible combinations of two-leak scenarios. Each zone is composed of at least two consecutive nodes, which means that the number of zones is at most half the number of nodes. With this segmentation, the leak identification task is to locate the zones where the pair of leaks are occurring. To quantify the uncertainty degree, a relaxation node criterion is used. The simulation results evidenced that the outcomes are accurate in most cases by using one-relaxation-node and two-relaxation-node criteria. Full article
(This article belongs to the Special Issue Application of Machine Learning in Urban Water Management: Recent Advances and Prospects)
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32 pages, 6056 KiB  
Article
Water Flow Modeling and Forecast in a Water Branch of Mexico City through ARIMA and Transfer Function Models for Anomaly Detection
by David Barrientos-Torres, Erick Axel Martinez-Ríos, Sergio A. Navarro-Tuch, Jose Luis Pablos-Hach and Rogelio Bustamante-Bello
Water 2023, 15(15), 2792; https://doi.org/10.3390/w15152792 - 2 Aug 2023
Cited by 2 | Viewed by 1741
Abstract
Early identification of anomalies (such as leakages or sensor failures) in urban water distribution systems is critical to mitigating water scarcity in cities and is a challenge in water resource management. Several data-driven methods based on machine learning algorithms have been proposed in [...] Read more.
Early identification of anomalies (such as leakages or sensor failures) in urban water distribution systems is critical to mitigating water scarcity in cities and is a challenge in water resource management. Several data-driven methods based on machine learning algorithms have been proposed in the literature for leakage detection in urban water distribution systems. Still, most of them are challenging to implement due to their complexity and requirements of vast amounts of reliable data for proper model generation. In addition, the required infrastructure and instrumentation to collect the data needed to train the models could be unaffordable. This paper presents the use and comparison of Autoregressive Integrated Moving Average models and Transfer Function models generated via the Box–Jenkins approach to modeling the water flow in water distribution systems for anomaly detection. The models were fit using water flow data from tanks operating in a branch of the water distribution system of Mexico City. The results showed that both methods helped select the best model type for each variable in the analyzed water branch, with Seasonal ARIMA models achieving a lower mean absolute percentage error than the fitted Transfer Function models. Furthermore, this methodology can be adjusted to different time windows to generate alerts at different rates and does not require a large sample size. The generated anomaly detection models could improve the efficiency of the water distribution system by detecting anomalies such as wrong measurements and water leakages. Full article
(This article belongs to the Special Issue Application of Machine Learning in Urban Water Management: Recent Advances and Prospects)
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17 pages, 5196 KiB  
Article
Application Research on Risk Assessment of Municipal Pipeline Network Based on Random Forest Machine Learning Algorithm
by Hang Cen, Delong Huang, Qiang Liu, Zhongling Zong and Aiping Tang
Water 2023, 15(10), 1964; https://doi.org/10.3390/w15101964 - 22 May 2023
Cited by 1 | Viewed by 1700
Abstract
Urban municipal water supply is an important part of underground pipelines, and their scale continues to expand. Due to the continuous improvement in the quality and quantity of data available for pipeline systems in recent years, traditional pipeline network risk assessment cannot cope [...] Read more.
Urban municipal water supply is an important part of underground pipelines, and their scale continues to expand. Due to the continuous improvement in the quality and quantity of data available for pipeline systems in recent years, traditional pipeline network risk assessment cannot cope with the improvement of various monitoring methods. Therefore, this paper proposes a machine learning-based risk assessment method for municipal pipe network operation and maintenance and builds a model example based on the data of a pipeline network base in a park in Suzhou. We optimized the random forest learning model, compared it with other centralized learning methods, and finally evaluated the model’s learning effect. Finally, the risk probability associated with each pipe segment sample was obtained, the risk factors affecting the pipe segment’s failure were determined, and their relevance and importance ranking was established. The results showed that the most influential factors are pipe material, soil properties, service life, and the number of past failures. The random forest algorithm demonstrated better prediction accuracy and robustness on the dataset. Full article
(This article belongs to the Special Issue Application of Machine Learning in Urban Water Management: Recent Advances and Prospects)
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22 pages, 11484 KiB  
Article
Online Control of the Raw Water System of a High-Sediment River Based on Deep Reinforcement Learning
by Zhaomin Li, Lu Bai, Wenchong Tian, Hexiang Yan, Wanting Hu, Kunlun Xin and Tao Tao
Water 2023, 15(6), 1131; https://doi.org/10.3390/w15061131 - 15 Mar 2023
Cited by 1 | Viewed by 2142
Abstract
Water supply systems that use rivers with high sedimentation levels may experience issues such as reservoir siltation. The suspended sediment concentration (SSC) of rivers experiences interannual variation and high nonlinearity due to its close relationship with meteorological factors, which increase the mismatch between [...] Read more.
Water supply systems that use rivers with high sedimentation levels may experience issues such as reservoir siltation. The suspended sediment concentration (SSC) of rivers experiences interannual variation and high nonlinearity due to its close relationship with meteorological factors, which increase the mismatch between the river water source and urban water demand. The raw water system scheduling problem is expressed as a reservoir and pump station control problem that involves real-time SSC changes. To lower the SSC of the water intake and lower the pumping station’s energy consumption, a deep reinforcement learning (DRL) model based on SSC prediction was developed. The framework consists of a DRL model, a hydraulic model for simulating the raw water system, and a neural network for predicting river SSC. The framework was tested using data from a Yellow River water withdrawal pumping station in China with an average capacity of 400,000 m3/d. The strategy created in this study can reduce the system energy consumption per unit of water withdrawal by 8.33% and the average annual water withdrawal SSC by 37.01%, when compared to manual strategy. Meanwhile, the deep reinforcement learning algorithm had good response robustness to uncertain imperfect predictive data. Full article
(This article belongs to the Special Issue Application of Machine Learning in Urban Water Management: Recent Advances and Prospects)
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