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Smart Water Solutions with Big Data
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Smart Water Solutions with Big Data

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 14304

Special Issue Editors

Prof. Dr. Mariana Mocanu

E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Romania
Interests: software engineering; distributed computing; data mining; skills and expertise; environmental analysis; system integration; environmental management system
Special Issues, Collections and Topics in MDPI journals
Dr. Costin-Gabriel Chiru

E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
Interests: natural language processing

Special Issue Information

Dear Colleagues,

The water industry is currently ushering in a period of rapid development of digital transfor-mation, and various countries continue to introduce new policies to promote the steady im-provement of the overall level of information technology application in the water industry. Improving water conservancy information infrastructure and actively promoting the application of 5G technology in water conservancy engineering safety monitoring and early warning work has become one of the key tasks of the world's water conservancy, and the construction of smart water affairs has gradually become a new development model of the times.

To solve above problems, this Special Issue raises the following questions:

  • At the strategic level, what is the smart water strategy and positioning of each country? How to match the strategy of the local government?
  • How to design a reasonable operation process and operation mechanism to ensure that the planning, construction, operation, and evaluation of the future smart water blueprint can be operated scientifically and efficiently?
  • Is the evolution path of the blueprint for smart water construction feasible? What are the clear projects or tasks?

In the future, new infrastructure represented by 5G, AI, and big data will complement the short-comings of water informatization and promote the reform of water informatization to deep water.

The solicitation of this Special Issue includes but is not limited to the following research papers:

  • Smart Water Affairs Digital Base
  • System architecture of smart water
  • Data fusion in the field of water affairs
  • Water industry application
  • Comprehensive governance capability of water industry

Prof. Dr. Mariana Mocanu
Dr. Costin-Gabriel Chiru
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

  • smart water solutions
  • water resource management
  • water distribution systems
  • big data
  • water modernization
  • water public service capacity
  • ICT Technology
  • comprehensive benefits of resource application
  • water distribution networks
  • drinking water plant
  • 5G
  • lora
  • RF networks
  • water conservancy
  • water contradiction
  • water conservancy project facility efficiency
  • comprehensive value of water construction
  • resource intensification

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

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20 pages, 4387 KiB  
Article
Advanced Strategies for Monitoring Water Consumption Patterns in Households Based on IoT and Machine Learning
by Diana Arsene, Alexandru Predescu, Bogdan Pahonțu, Costin Gabriel Chiru, Elena-Simona Apostol and Ciprian-Octavian Truică
Water 2022, 14(14), 2187; https://doi.org/10.3390/w14142187 - 11 Jul 2022
Cited by 17 | Viewed by 5393
Abstract
Water resource management represents a fundamental aspect of a modern society. Urban areas present multiple challenges requiring complex solutions, which include multidomain approaches related to the integration of advanced technologies. Water consumption monitoring applications play a significant role in increasing awareness, while machine [...] Read more.
Water resource management represents a fundamental aspect of a modern society. Urban areas present multiple challenges requiring complex solutions, which include multidomain approaches related to the integration of advanced technologies. Water consumption monitoring applications play a significant role in increasing awareness, while machine learning has been proven for the design of intelligent solutions in this field. This paper presents an approach for monitoring and predicting water consumption from the most important water outlets in a household based on a proposed IoT solution. Data processing pipelines were defined, including K-means clustering and evaluation metrics, extracting consumption events, and training classification methods for predicting consumption sources. Continuous water consumption monitoring offers multiple benefits toward improving decision support by combining modern processing techniques, algorithms, and methods. Full article
(This article belongs to the Special Issue Smart Water Solutions with Big Data)
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12 pages, 1349 KiB  
Article
A Lagrangian Backward Air Parcel Trajectories Clustering Framework
by Iulia-Maria Rădulescu, Alexandru Boicea, Florin Rădulescu and Daniel-Călin Popeangă
Water 2021, 13(24), 3638; https://doi.org/10.3390/w13243638 - 17 Dec 2021
Viewed by 2845
Abstract
Many studies concerning atmosphere moisture paths use Lagrangian backward air parcel trajectories to determine the humidity sources for specific locations. Automatically grouping trajectories according to their geographical position simplifies and speeds up their analysis. In this paper, we propose a framework for clustering [...] Read more.
Many studies concerning atmosphere moisture paths use Lagrangian backward air parcel trajectories to determine the humidity sources for specific locations. Automatically grouping trajectories according to their geographical position simplifies and speeds up their analysis. In this paper, we propose a framework for clustering Lagrangian backward air parcel trajectories, from trajectory generation to cluster accuracy evaluation. We employ a novel clustering algorithm, called DenLAC, to cluster troposphere air currents trajectories. Our main contribution is representing trajectories as a one-dimensional array consisting of each trajectory’s points position vector directions. We empirically test our pipeline by employing it on several Lagrangian backward trajectories initiated from Břeclav District, Czech Republic. Full article
(This article belongs to the Special Issue Smart Water Solutions with Big Data)
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28 pages, 2229 KiB  
Article
A Machine Learning Framework for Olive Farms Profit Prediction
by Panagiotis Christias and Mariana Mocanu
Water 2021, 13(23), 3461; https://doi.org/10.3390/w13233461 - 6 Dec 2021
Cited by 6 | Viewed by 3914
Abstract
Agricultural systems are constantly stressed due to higher demands for products. Consequently, water resources consumed on irrigation are increased. In combination with the climatic change, those are major obstacles to maintaining sustainable development, especially in a semi-arid land. This paper presents an end-to-end [...] Read more.
Agricultural systems are constantly stressed due to higher demands for products. Consequently, water resources consumed on irrigation are increased. In combination with the climatic change, those are major obstacles to maintaining sustainable development, especially in a semi-arid land. This paper presents an end-to-end Machine Learning framework for predicting the potential profit from olive farms. The objective is to estimate the optimal economic gain while preserving water resources on irrigation by considering various related factors such as climatic conditions, crop management practices, soil characteristics, and crop yield. The case study focuses on olive tree farms located on the Hellenic Island of Crete. Real data from the farms and the weather in the area will be used. The target is to build a framework that will preprocess input data, compare the results among a group of Machine Learning algorithms and propose the best-predicted value of economic profit. Various aspects during this process will be thoroughly examined such as the bias-variance tradeoff and the problem of overfitting, data transforms, feature engineering and selection, ensemble methods as well as pursuing optimal resampling towards better model accuracy. Results indicated that through data preprocessing and resampling, Machine Learning algorithms performance is enhanced. Ultimately, prediction accuracy and reliability are greatly improved compared to algorithms’ performances without the framework’s operation. Full article
(This article belongs to the Special Issue Smart Water Solutions with Big Data)
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19 pages, 1068 KiB  
Article
Change Point Enhanced Anomaly Detection for IoT Time Series Data
by Elena-Simona Apostol, Ciprian-Octavian Truică, Florin Pop and Christian Esposito
Water 2021, 13(12), 1633; https://doi.org/10.3390/w13121633 - 10 Jun 2021
Cited by 26 | Viewed by 6319
Abstract
Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly [...] Read more.
Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance. Full article
(This article belongs to the Special Issue Smart Water Solutions with Big Data)
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Review

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32 pages, 1828 KiB  
Review
Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview
by Anca Hangan, Costin-Gabriel Chiru, Diana Arsene, Zoltan Czako, Dragos Florin Lisman, Mariana Mocanu, Bogdan Pahontu, Alexandru Predescu and Gheorghe Sebestyen
Water 2022, 14(14), 2174; https://doi.org/10.3390/w14142174 - 9 Jul 2022
Cited by 16 | Viewed by 5660
Abstract
Water supply systems are essential for a modern society. This article presents an overview of the latest research related to information and communication technology systems for water resource monitoring, control and management. The main objective of our review is to show how emerging [...] Read more.
Water supply systems are essential for a modern society. This article presents an overview of the latest research related to information and communication technology systems for water resource monitoring, control and management. The main objective of our review is to show how emerging technologies offer support for smart administration of water infrastructures. The paper covers research results related to smart cities, smart water monitoring, big data, data analysis and decision support. Our evaluation reveals that there are many possible solutions generated through combinations of advanced methods. Emerging technologies open new possibilities for including new functionalities such as social involvement in water resource management. This review offers support for researchers in the area of water monitoring and management to identify useful models and technologies for designing better solutions. Full article
(This article belongs to the Special Issue Smart Water Solutions with Big Data)
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