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Innovative Data Analysis Methodologies in the Water Sector: Water Quality...
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Innovative Data Analysis Methodologies in the Water Sector: Water Quality and Water Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 9409

Special Issue Editors

Prof. Dr. Jorge Rodríguez-Chueca

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Guest Editor
Departamento de Ingeniería Química Industrial y del Medio Ambiente, E.T.S. de Ingenieros Industriales, Universidad Politécnica de Madrid, c/José Gutiérrez Abascal 2, 28006 Madrid, Spain
Interests: water and wastewater treatment; water quality; water management; wastewater reuse; advanced treatments; environmental engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. David J. Vicente González

E-Mail Website
Guest Editor
Internation Centre for Numerical Methods in Engineering (CIMNE), Barcelona, Spain
Interests: water distribution systems; leakage management; machine learning; numerical methods applied to engineering
Dr. Elena Torfs

E-Mail Website
Guest Editor
Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
Interests: mathematical modelling; hybrid models; process control; potable water; water distribution networks; wastewater treatment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main objective of this Special Issue is to show the scientific community how new innovative data analysis methodologies (e.g., machine learning, deep learning, artificial intelligence, blockchain, etc.) can be of great help for the management and quality of water resources, and complement classical management methodologies.

These types of methodologies can be used to predict water demands, distribution system failures, selection of treatment technologies, prediction of the behaviour of a given pollutant, and so on.

Although they are increasingly present in the water sector, their real application is still limited in certain areas such as treatment management.

The impact of global population growth, coupled with increased human activity, on the natural environment is leading to increased water stress in many parts of the world. This situation will be aggravated in the coming decades as a consequence of climate change and a more irregular water regime. For this reason, there is an increasing need for excellent water resource management to maximise the use of water resources with the least use of external resources. In recent years, the growth in technological knowledge has enabled the development of innovative tools for data analysis (e.g. artificial intelligence, machine learning or deep learning), which can become our allies in achieving optimal water resource management.

The aim of this Special Issue on “Innovative data analysis methodologies in the water sector: water quality and water management” is to present the state-of-the-art related but not limited to the application of these innovative technologies both in the urban water management and natural water resources.

We invite authors to submit research articles, reviews, communications, and concept papers that demonstrate the high potential of these methodologies in the water sector.

Prof. Dr. Jorge Rodríguez-Chueca
Prof. Dr. David J. Vicente González
Prof. Dr. Elena Torfs
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

  • Artificial Intelligence
  • machine learning
  • deep learning
  • blockchain
  • water quality management
  • water treatment

Published Papers (3 papers)

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Research

35 pages, 13059 KiB  
Article
Water Consumption Pattern Analysis Using Biclustering: When, Why and How
by Miguel G. Silva, Sara C. Madeira and Rui Henriques
Water 2022, 14(12), 1954; https://doi.org/10.3390/w14121954 - 18 Jun 2022
Cited by 3 | Viewed by 2430
Abstract
Sensors deployed within water distribution systems collect consumption data that enable the application of data analysis techniques to extract essential information. Time series clustering has been traditionally applied for modeling end-user water consumption profiles to aid water management. However, its effectiveness is limited [...] Read more.
Sensors deployed within water distribution systems collect consumption data that enable the application of data analysis techniques to extract essential information. Time series clustering has been traditionally applied for modeling end-user water consumption profiles to aid water management. However, its effectiveness is limited by the diversity and local nature of consumption patterns. In addition, existing techniques cannot adequately handle changes in household composition, disruptive events (e.g., vacations), and consumption dynamics at different time scales. In this context, biclustering approaches provide a natural alternative to detect groups of end-users with coherent consumption profiles during local time periods while addressing the aforementioned limitations. This work discusses when, why and how to apply biclustering techniques for water consumption data analysis, and further proposes a methodology to this end. To the best of our knowledge, this is the first work introducing biclustering to water consumption data analysis. Results on data from a real-world water distribution system—Quinta do Lago, Portugal—confirm the potentialities of the proposed approach for pattern discovery with guarantees of statistical significance and robustness that entities can rely on for strategic planning. Full article
(This article belongs to the Special Issue Innovative Data Analysis Methodologies in the Water Sector: Water Quality and Water Management)
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15 pages, 1764 KiB  
Article
Modeling Groundwater Nitrate Contamination Using Artificial Neural Networks
by Christina Stylianoudaki, Ioannis Trichakis and George P. Karatzas
Water 2022, 14(7), 1173; https://doi.org/10.3390/w14071173 - 06 Apr 2022
Cited by 8 | Viewed by 2643
Abstract
The scope of the present study is the estimation of the concentration of nitrates (NO3−) in groundwater using artificial neural networks (ANNs) based on easily measurable in situ data. For the purpose of the current study, two feedforward [...] Read more.
The scope of the present study is the estimation of the concentration of nitrates (NO3−) in groundwater using artificial neural networks (ANNs) based on easily measurable in situ data. For the purpose of the current study, two feedforward neural networks were developed to determine whether including land use variables would improve the model results. In the first network, easily measurable field data were used, i.e., pH, electrical conductivity, water temperature, air temperature, and aquifer level. This model achieved a fairly good simulation based on the root mean squared error (RMSE in mg/L) and the Nash–Sutcliffe Model Efficiency (NSE) indicators (RMSE = 26.18, NSE = 0.54). In the second model, the percentages of different land uses in a radius of 1000 m from each well was included in an attempt to obtain a better description of nitrate transport in the aquifer system. When these variables were used, the performance of the model increased significantly (RMSE = 15.95, NSE = 0.70). For the development of the models, data from chemical and physical analyses of groundwater samples from wells located in the Kopaidian Plain and the wider area of the Asopos River Basin, both in Greece, were used. The simulation that the models achieved indicates that they are a potentially useful tools for the estimation of groundwater contamination by nitrates and may therefore constitute a basis for the development of groundwater management plans. Full article
(This article belongs to the Special Issue Innovative Data Analysis Methodologies in the Water Sector: Water Quality and Water Management)
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14 pages, 4528 KiB  
Article
Nitrate in Groundwater Resources of Hormozgan Province, Southern Iran: Concentration Estimation, Distribution and Probabilistic Health Risk Assessment Using Monte Carlo Simulation
by Amin Mohammadpour, Ehsan Gharehchahi, Ahmad Badeenezhad, Iman Parseh, Razieh Khaksefidi, Mohammad Golaki, Reza Dehbandi, Abooalfazl Azhdarpoor, Zahra Derakhshan, Jorge Rodriguez-Chueca and Stefanos Giannakis
Water 2022, 14(4), 564; https://doi.org/10.3390/w14040564 - 13 Feb 2022
Cited by 22 | Viewed by 3559
Abstract
High nitrate concentration in drinking water has the potential to cause a series of harmful effects on human health. This study aims to evaluate the health risk of nitrate in groundwater resources of Hormozgan province in four age groups, including infants, children, teenagers, [...] Read more.
High nitrate concentration in drinking water has the potential to cause a series of harmful effects on human health. This study aims to evaluate the health risk of nitrate in groundwater resources of Hormozgan province in four age groups, including infants, children, teenagers, and adults, based on the US EPA methodology and Monte Carlo technique to assess uncertainty and sensitivity analysis. A Geographic Information System (GIS) was used to investigate the spatial distribution of nitrate levels in the study area. The nitrate concentration ranged from 0.3 to 30 mg/L, with an average of 7.37 ± 5.61 mg/L. There was no significant difference between the average concentration of nitrate in all study areas (p > 0.05). The hazard quotient (HQ) was less than 1 for all age groups and counties, indicating a low-risk level. The HQ95 for infants and children in the Monte Carlo simulation was 1.34 and 1.22, respectively. The sensitivity analysis findings showed that the parameter with the most significant influence on the risk of toxicity in all age groups was the nitrate content. Therefore, implementing a water resources management program in the study area can reduce nitrate concentration and enhance water quality. Full article
(This article belongs to the Special Issue Innovative Data Analysis Methodologies in the Water Sector: Water Quality and Water Management)
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