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Water Modeling Using Combined Machine Learning and Fieldwork Investigation

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: 31 October 2025 | Viewed by 919

Special Issue Editors


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Guest Editor
Department of Geosciences, University of Texas Permian Basin, Odessa, TX 79762, USA
Interests: environmental science; energy sustainability; geology and geophysics; machine learning; modeling investigation; field works; geoinformatics
Department of Civil Engineering, New Mexico State University, Las Cruces, NM 88003, USA
Interests: smart infrastructure; water resources engineering; hydrologic modeling; GIS; machine learning
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Guest Editor
Oklahoma Geological Survey, University of Oklahoma, Norman, OK 73019, USA
Interests: environmental hazards; machine learning application; earth resources engineering; GIS; soil-landscape modeling

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the intersection of computational modeling and advanced machine learning techniques in the context of hydrological systems. The primary focus is to highlight innovative approaches where machine learning is applied to enhance predictive models of water dynamics, optimize water resource management, and improve our understanding of environmental water systems. Submissions are invited that cover a range of topics, including, but not limited to, predictive modeling of water quality and quantity, flood forecasting, groundwater monitoring, and the integration of real-time data analytics in hydrological models.

This Special Issue contributes to the growing body of literature that seeks to bridge traditional water modeling approaches with cutting-edge machine learning methods. By situating itself at the convergence of these fields, it seeks to advance research in areas where machine learning can offer novel insights into complex water-related challenges. Existing research has focused on the use of either water modeling or machine learning independently; however, this issue calls for contributions that demonstrate the synergistic potential when both are combined to address multifaceted problems in water science and management.

Dr. Joonghyeok Heo
Dr. Huidae Cho
Dr. Netra Regmi
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

  • machine learning
  • field survey
  • water modeling
  • big data analysis
  • geostatistic analysis
  • water quality and water quantity
  • environmental sustainability

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Published Papers (1 paper)

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Research

18 pages, 4380 KiB  
Article
Deep Learning-Based Retrieval of Chlorophyll-a in Lakes Using Sentinel-1 and Sentinel-2 Satellite Imagery
by Bongseok Jeong, Sunmin Lee, Joonghyeok Heo, Jeongho Lee and Moung-Jin Lee
Water 2025, 17(11), 1718; https://doi.org/10.3390/w17111718 - 5 Jun 2025
Viewed by 476
Abstract
Remote sensing and AI models have been utilized for monitoring Chlorophyll-a (Chl-a), a primary indicator of eutrophication across broad water bodies. Previous studies have primarily relied on optical remote sensing data for assessing Chl-a’s spectral characteristics. Synthetic-aperture radar (SAR) data, which contain valuable [...] Read more.
Remote sensing and AI models have been utilized for monitoring Chlorophyll-a (Chl-a), a primary indicator of eutrophication across broad water bodies. Previous studies have primarily relied on optical remote sensing data for assessing Chl-a’s spectral characteristics. Synthetic-aperture radar (SAR) data, which contain valuable information about surface algae containing Chl-a, remains underutilized despite its high potential for improving Chl-a retrieval accuracy. Therefore, this study aims to develop a Convolutional neural network (CNN) based Chl-a retrieval model utilizing both SAR data and optical data in Korean lakes. The model dataset was established by acquiring Chl-a concentration data and Sentinel-1/2 imagery from the Copernicus Open Access Hub. The CNN model trained on both optical and SAR data exhibited superior performance (R2 = 0.7992, RMSE = 10.3282 mg/m3, RPD = 2.2315) compared with the model trained exclusively on optical data. Moreover, SAR data exhibited moderate variable importance among all variables, demonstrating their efficacy as input variables for Chl-a concentration estimation. Furthermore, the CNN model estimated Chl-a concentrations with a spatial distribution that matched the observed spatial heterogeneity of Chl-a concentrations. These results are expected to serve as a foundation for future research on remote monitoring of Chl-a using such data. Full article
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