Artificial Intelligence in Hydrological Sciences: Opportunities, Prospects, and Concerns
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".
Deadline for manuscript submissions: 10 January 2025 | Viewed by 276
Special Issue Editors
Interests: water resources; hydrology; AI; climate change; sustainable development; time series; hydrological modeling; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: water erosion; sediment transport; hydrology; environmental modeling; numer-ical methods; water resources
Special Issues, Collections and Topics in MDPI journals
Interests: hydrology; sediment transport; water quality; watershed management; climate change; water resources
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of artificial intelligence (AI) into hydrological science is revolutionizing our understanding and management of water resources. This Special Issue, titled "Artificial Intelligence in Hydrological Sciences: Opportunities, Prospects, and Concerns", will explore multifaceted applications of AI in this critical field. Through the development of advanced AI algorithms, big data analytics, and predictive modeling, AI offers unprecedented opportunities for enhancing and improving the overall efficiency of water management practices.
We are pleased to invite you to contribute to this Special Issue, which will explore diverse applications of AI in water science. Our goal is to present a comprehensive overview of how AI technologies are transforming water science and management, highlighting both the opportunities and the challenges involved. Through this collection of works, we aim to showcase innovative solutions, theoretical advancements, and practical case studies that shed light on the current state and future prospects of AI applications in hydrological science.
This Special Issue aims to explore and highlight transformative opportunities, prospects, and concerns regarding AI in water science. It will bridge the gap between theoretical developments and practical implementations, offering insights into the real-world impacts of AI on water science. By presenting state-of-the-art research and critical analyses, this Special Issue will contribute to the ongoing discourse on the role of AI in sustainable water management and climate resilience.
We look forward to receiving your contributions.
Dr. Hossein Bonakdari
Dr. Silvio José Gumiere
Prof. Dr. Bahram Gharabaghi
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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