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Contaminants in Water Systems: Intelligent Recognition, Detection and Analytical Methods
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Contaminants in Water Systems: Intelligent Recognition, Detection and Analytical Methods

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 815

Special Issue Editors

Dr. Jie Yu

E-Mail Website
Guest Editor
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Interests: water enivorment monitoring; optical sensing; artificial intelligence; three-dimensional fluorescence
Prof. Dr. Dibo Hou

E-Mail Website
Guest Editor
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Interests: smart perception and advanced sensing; environmental monitoring and early warning; robotics and unmanned systems
Dr. Xiaoyu Tang

E-Mail Website
Guest Editor
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Interests: detection technology and automation equipment; process detection and information processing; complex fluid monitoring; flow field imaging; machine learning
Dr. Ke Wang

E-Mail Website
Guest Editor Assistant
College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China
Interests: data mining; artificial intelligence; water environment management; information systems

Special Issue Information

Dear Colleagues,

Water is essential for all known forms of life, and its quality is vital for human health, agricultural productivity and ecological balance. However, water systems worldwide are increasingly becoming contaminated by various pollutants, posing significant risks to public health and the environment. To address these challenges, intelligent recognition, detection and analytical methods are being developed and deployed to monitor and manage water systems effectively.

This Special Issue seeks high-quality works focusing on advanced techniques for identifying and quantifying pollutants, innovative detection methodologies and analytical strategies that leverage intelligent systems for monitoring and mitigating contaminants in various water sources.

Topics include, but are not limited to, the following:

  1. Development and application of novel sensors for contaminant detection in water systems;
  2. Intelligent algorithms and machine learning models for the real-time monitoring and prediction of water quality;
  3. Analytical methods for the identification and quantification of emerging contaminants;
  4. Integration of IoT (Internet of Things) technologies in water quality management;
  5. Advances in spectroscopic and chromatographic techniques for water analysis;
  6. Case studies on the implementation of intelligent systems in municipal and industrial water treatment;
  7. Data-driven approaches for assessing the impact of contaminants on public health and ecosystems;
  8. Remote sensing technologies for large-scale water quality monitoring;
  9. Innovations in portable and field-deployable detection devices for rapid contaminant assessment

Dr. Jie Yu
Prof. Dr. Dibo Hou
Dr. Xiaoyu Tang
Guest Editors

Dr. Ke Wang
Guest Editor Assistant

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. Processes is an international peer-reviewed open access monthly 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 2400 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 contaminant recognition
  • analytical techniques
  • water quality monitoring
  • machine learning
  • IoT in water management
  • spectroscopy and chromatography
  • emerging pollutants
  • remote sensing

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

Published Papers (1 paper)

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Research

21 pages, 5660 KiB  
Article
EWAIS: An Ensemble Learning and Explainable AI Approach for Water Quality Classification Toward IoT-Enabled Systems
by Nermeen Gamal Rezk, Samah Alshathri, Amged Sayed and Ezz El-Din Hemdan
Processes 2024, 12(12), 2771; https://doi.org/10.3390/pr12122771 - 5 Dec 2024
Viewed by 620
Abstract
In the context of smart cities with advanced Internet of Things (IoT) systems, ensuring the sustainability and safety of freshwater resources is pivotal for public health and urban resilience. This study introduces EWAIS (Ensemble Learning and Explainable AI System), a novel framework designed [...] Read more.
In the context of smart cities with advanced Internet of Things (IoT) systems, ensuring the sustainability and safety of freshwater resources is pivotal for public health and urban resilience. This study introduces EWAIS (Ensemble Learning and Explainable AI System), a novel framework designed for the smart monitoring and assessment of water quality. Leveraging the strengths of Ensemble Learning models and Explainable Artificial Intelligence (XAI), EWAIS not only enhances the prediction accuracy of water quality but also provides transparent insights into the factors influencing these predictions. EWAIS integrates multiple Ensemble Learning models—Extra Trees Classifier (ETC), K-Nearest Neighbors (KNN), AdaBoost Classifier, decision tree (DT), Stacked Ensemble, and Voting Ensemble Learning (VEL)—to classify water as drinkable or non-drinkable. The system incorporates advanced techniques for handling missing data and statistical analysis, ensuring robust performance even in complex urban datasets. To address the opacity of traditional Machine Learning models, EWAIS employs XAI methods such as SHAP and LIME, generating intuitive visual explanations like force plots, summary plots, dependency plots, and decision plots. The system achieves high predictive performance, with the VEL model reaching an accuracy of 0.89 and an F1-Score of 0.85, alongside precision and recall scores of 0.85 and 0.86, respectively. These results demonstrate the proposed framework’s capability to deliver both accurate water quality predictions and actionable insights for decision-makers. By providing a transparent and interpretable monitoring system, EWAIS supports informed water management strategies, contributing to the sustainability and well-being of urban populations. This framework has been validated using controlled datasets, with IoT implementation suggested to enhance water quality monitoring in smart city environments. Full article
(This article belongs to the Special Issue Contaminants in Water Systems: Intelligent Recognition, Detection and Analytical Methods)
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