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Intelligent Water Management: Machine Learning, Remote Sensing, Data Analytics, Predictive Modeling, and the Path to Sustainability

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

Deadline for manuscript submissions: 20 October 2025 | Viewed by 835

Special Issue Editors


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Guest Editor
Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: water resources; hydrology; AI; climate change; sustainable development; time series; hydrological modelling; machine learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Soil and Agri-Food Engineering, Universite Laval, Québec, QC G1V 0A6, Canada
Interests: climate change; drought management; soil and water conservation; irrigation; hydrological modeling; surface hydrology; rainfall runoff modeling; hydraulics; numerical modeling; hydrology; hydrologic and water resource management; environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water is at the heart of humanity’s resilience in an era of climate uncertainty and resource scarcity. This Special Issue invites pioneering research that harnesses the power of artificial intelligence (AI) and advanced data technologies to revolutionize how we monitor, predict, and manage water systems. From real-time water quality monitoring and flood forecasting to drought prediction and water distribution optimization, we explore how machine learning, remote sensing, big data analytics, and predictive modeling can unlock smarter, more sustainable solutions. This collection aims to bridge the gap between traditional hydrological approaches and next-generation tools, fostering climate resilience, enhancing water resource management, and safeguarding ecosystems. Situated at the intersection of environmental science and cutting-edge technology, this Special Issue builds on the existing literature by spotlighting AI-driven forecasting, smart water systems, and data-driven decision-making as catalysts for transformative change. We welcome contributions that push boundaries—whether through theoretical breakthroughs or real-world applications—offering a dynamic platform for researchers, engineers, and policymakers to shape the future of water sustainability. Dive in and join us on the path to a water-secure world!

We look forward to receiving your contributions.

Dr. Hossein Bonakdari
Prof. Dr. Silvio José Gumiere
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 (AI)
  • water quality monitoring
  • predictive modeling
  • flood forecasting
  • drought prediction
  • water resource management
  • big data analytics
  • climate resilience
  • sustainable water management
  • environmental science
  • hydrological modeling
  • machine learning
  • remote sensing
  • data-driven decision-making
  • smart water systems
  • water distribution optimization
  • AI-driven forecasting
  • ecosystem management
  • water conservation
  • water infrastructure management

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Published Papers (1 paper)

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Review

28 pages, 1185 KB  
Review
A Review of Water Quality Forecasting Models for Freshwater Lentic Ecosystems
by Jovheiry Christopher García-Guerrero, José M. Álvarez-Alvarado, Roberto Valentín Carrillo-Serrano, Viviana Palos-Barba and Juvenal Rodríguez-Reséndiz
Water 2025, 17(15), 2312; https://doi.org/10.3390/w17152312 - 4 Aug 2025
Viewed by 485
Abstract
Water quality (WQ) monitoring is critical for Mexico and the world due to water pollution and scarcity problems in recent years. In this article, a systematic review was conducted considering only forecasting models focused on lentic freshwater bodies (to specialize the analysis of [...] Read more.
Water quality (WQ) monitoring is critical for Mexico and the world due to water pollution and scarcity problems in recent years. In this article, a systematic review was conducted considering only forecasting models focused on lentic freshwater bodies (to specialize the analysis of variables, problems, considerations, etc.) from 2019 to 2025 (to ensure the inclusion of the most relevant and new studies). This review analyzes 52 articles focused on the monitoring place, predictors, forecasted variables, configuration of each forecasting model, results with or without multiple forecast horizons, monitoring conditions, forecasting horizon, data availability, and model replicability. Our review shows that the main models documented used to predict WQ are based on machine learning (where RFs are the most used), AI (where ANNs are the most used and LSTM-based architectures are the most implemented), and statistical methods (where MLR is the most used). The principal forecasted WQ variables are Chl-α, DO, and TP. In comparison, the most used predictors are TP, temperature, and Chl-α. Furthermore, only 10 articles have made their databases available, and nine articles share the configuration of their models. Future research should investigate the real impact of data (quantity and inputs) variation in forecasting values for multiple forecast horizons. Full article
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