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Towards Sustainability: Applications of Machine Learning in Water Management and Environmental Monitoring

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: 30 August 2025 | Viewed by 1587

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Special Issue Editors

Dr. Soufiane Haddout

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Guest Editor

Department of Physics, Faculty of Science, Ibn Tofail University, Kenitra, Morocco
Interests: environmental fluid mechanics; hydrodynamic modeling of estuaries; hydraulic modeling; numerical modeling; analytical modeling; transport modeling; water managements; limnology and oceanography; optimization methods

Prof. Dr. Mariusz Ptak

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Guest Editor

Department of Hydrology and Water Management, Adam Mickiewicz University, 61-680 Poznań, Poland
Interests: hydrology; lakes; rivers; water temperature; water level; ice cover; water resources; climate changes
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Special Issue Information

Dear Colleagues,

The Special Issue “Towards Sustainability: Applications of Machine Learning in Water Management and Environmental Monitoring” aims to explore the intersection of machine learning technologies and their applications in water management and environmental monitoring.

Focus, Scope, and Purpose:

Focus: This Special Issue focuses on the innovative use of machine learning to address complex challenges in water management and environmental monitoring. It seeks to highlight advancements in predictive analytics, data-driven decision making, and the development of sustainable practices through machine learning.
Scope: The scope includes, but is not limited to, case studies, theoretical developments, practical applications, and reviews of machine learning methodologies in the context of hydrology, water quality assessment, resource management, and environmental sustainability.
Purpose: The purpose is to showcase how machine learning can enhance the efficiency, accuracy, and sustainability of water management and environmental monitoring practices. It aims to bring together interdisciplinary research that contributes to sustainable development goals.

Relation to Existing Literature:

This Special Issue will supplement the existing literature by providing a focused platform for the dissemination of research at the nexus of machine learning and sustainability. While there is a growing body of work on machine learning applications in various fields, this Special Issue will specifically address their role in fostering sustainable water management practices and environmental stewardship. It will fill gaps by providing empirical evidence, theoretical insights, and practical applications that demonstrate the tangible benefits of machine learning in promoting sustainability.

In line with the journal’s mission, this Special Issue will address challenges related to sustainability through socio-economic, scientific, and integrated approaches. By defining and quantifying sustainability, measuring and monitoring progress, and exploring relevant tools, applications, policies, and laws, this Special Issue will contribute significantly to the discourse on sustainable development.

Dr. Soufiane Haddout
Prof. Dr. Mariusz Ptak
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. Sustainability 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 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

machine learning
water management
environmental monitoring
sustainability
predictive analytics
hydrology
water quality assessment
data-driven decision making
sustainable development
environmental stewardship

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Published Papers (2 papers)

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Research

16 pages, 2857 KiB

Open AccessArticle

Bridging Sustainability and Environmental Impact Assessment: Multi-Scale Bioindication and Remote Sensing for Pollution Monitoring in Agroecosystems

by Mohammed Ajaoud, Cristiano Ciccarelli, Marco De Mizio, Massimiliano Gargiulo, Sara Parrilli, Claudia Savarese, Francesco Tufano and Massimiliano Lega

Sustainability 2025, 17(9), 4115; https://doi.org/10.3390/su17094115 (registering DOI) - 1 May 2025

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Abstract

Persistent environmental contaminants pose a substantial threat to agricultural ecosystems, necessitating robust methodologies for evaluation and mitigation of their effects. This study establishes a direct correlation between environmental impact assessment and sustainable agricultural management, showing the feasibility of using multi-scale bioindication and remote sensing technology to effectively monitor the impact of soil pollution in agricultural ecosystems. The key values of this research lie in the ability of the described approach to integrate advanced proximal/remote sensing and in situ analyses to assess the effects of soil contamination on bioindicators, providing a comprehensive framework for evaluating environmental stressors. The proposed methodology was tested on maize (Zea mays L.) and employs unmanned aerial vehicle-based multi/hyperspectral and thermal imaging to detect vegetation stress indicators such as normalized difference vegetation index and thermal anomalies. The interdisciplinary approach adopted in this research significantly enhances the value of the study by not only focusing on isolated results but also validating the entire methodological workflow. This cross-disciplinary integration ensures that the workflow retains its relevance across various environmental scenarios, enriching the results’ applicability and providing a robust framework for ongoing studies. The research objective of this work was achieved through experimental tests on soils contaminated with heavy metals and organic pollutants exceeding regulatory thresholds that revealed distinct spectral and thermal signatures, demonstrating the efficacy of integrated sensing for detailed environmental assessment. The findings underscore the role of bioindicators as pivotal tools for bridging environmental monitoring and sustainability by providing actionable insights into pollutant impacts and their cascading effects on ecosystems and human health. By equipping stakeholders with precise contamination detection tools, this study aims to provide a methodological approach to expand environmental impact assessment frameworks, supporting sustainable decision-making and risk management. These methodologies contribute to aligning agricultural practices with broader sustainability objectives, ensuring resilient food systems and ecosystem health. Full article

(This article belongs to the Special Issue Towards Sustainability: Applications of Machine Learning in Water Management and Environmental Monitoring)

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31 pages, 6940 KiB

Open AccessArticle

Short-Wave Infrared Spectroscopy for On-Site Discrimination of Hazardous Mineral Fibers Using Machine Learning Techniques

by Giuseppe Bonifazi, Sergio Bellagamba, Giuseppe Capobianco, Riccardo Gasbarrone, Ivano Lonigro, Sergio Malinconico, Federica Paglietti and Silvia Serranti

Sustainability 2025, 17(3), 972; https://doi.org/10.3390/su17030972 - 24 Jan 2025

Viewed by 1010

Abstract

Asbestos fibers are well-known carcinogens, and their rapid detection is critical for ensuring safety, protecting public health, and promoting environmental sustainability. In this work, short-wave infrared (SWIR) spectroscopy, combined with machine learning (ML), was evaluated as an environmentally friendly analytical approach for simultaneously distinguishing the asbestos type, asbestos-containing materials in various forms, asbestos-contaminated/-uncontaminated soil, and asbestos-contaminated/-uncontaminated cement, simultaneously. This approach offers a noninvasive and efficient alternative to traditional laboratory methods, aligning with sustainable practices by reducing hazardous waste generation and enabling in situ testing. Different chemometrics techniques were applied to discriminate the material classes. In more detail, partial least squares discriminant analysis (PLS-DA), principal component analysis-based discriminant analysis (PCA-DA), principal component analysis-based K-nearest neighbors classification (PCA-KNN), classification and regression trees (CART), and error-correcting output-coding support vector machine (ECOC SVM) classifiers were tested. The tested classifiers showed different performances in discriminating between the analyzed samples. CART and ECOC SVM performed best (

{R e c a l l}_{M}

and

{A c c u r a c y}_{M}

equal to 1.00), followed by PCA-KNN (

{R e c a l l}_{M}

of 0.98–1.00 and

{A c c u r a c y}_{M}

equal to 1.00). Poorer performances were obtained by PLS-DA (

{R e c a l l}_{M}

of 0.68–0.72 and

{A c c u r a c y}_{M}

equal to 0.95) and PCA-DA (

{R e c a l l}_{M}

of 0.66–0.70 and

{A c c u r a c y}_{M}

equal to 0.95). This research aligns with the United Nations’ Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-Being), by enhancing human health protection through advanced asbestos detection methods, and SDG 12 (Responsible Consumption and Production), by promoting sustainable, low-waste testing methodologies. Full article