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: 23 April 2025 | Viewed by 29
Special Issue Editors
Interests: environmental science; energy sustainability; geology and geophysics; machine learning; modeling investigation; field works; geoinformatics
Interests: smart infrastructure; water resources engineering; hydrologic modeling; GIS; machine learning
Special Issues, Collections and Topics in MDPI journals
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
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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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