Next-Generation Iceberg Behavior Modeling: Integrating Machine Learning, Computational Fluid Dynamics, and Environmental Dynamics
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Oceans and Coastal Zones".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 588
Special Issue Editor
Interests: Arctic geohazards; iceberg–structure–seabed interaction; computational fluid dy-namics; machine learning; AI applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Icebergs play a critical role in oceanic and climate systems, posing both challenges and opportunities for marine navigation, offshore engineering, and environmental sustainability. Traditional iceberg modeling methods, while effective, often struggle to capture the complex interactions between icebergs, ocean currents, wind forces, and climate dynamics. The integration of machine learning (ML), computational fluid dynamics (CFD), and environmental dynamics presents a cutting-edge approach to enhancing the accuracy, efficiency, and predictive capabilities of iceberg behavior models.
This Special Issue aims to gather interdisciplinary research that advances the state of the art in iceberg modeling. We welcome contributions that explore novel computational techniques, data-driven methodologies, and hybrid approaches to better understand iceberg drift, deterioration, and interactions with marine structures.
The topics of interest include, but are not limited to, the following:
- Machine learning and AI-driven approaches for iceberg tracking and prediction;
- Computational fluid dynamics (CFD) modeling of iceberg–ocean interactions;
- Environmental and climatic influences on iceberg dynamics;
- Remote sensing and data assimilation techniques for iceberg behavior analysis;
- Hybrid modeling techniques combining ML, CFD, and empirical data;
- Risk assessment and impact analysis for offshore operations and shipping;
- Advances in numerical simulations for iceberg melting, breakage, and stability.
We invite researchers from diverse fields, including oceanography, climate science, computational modeling, and artificial intelligence, to contribute to this Special Issue. Submissions may include original research articles, review papers, and case studies that push the boundaries of iceberg modeling.
Dr. Hamed Azimi
Guest Editor
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
- iceberg modeling
- machine learning
- computational fluid dynamics (CFD)
- ocean–ice interactions
- climate impact on icebergs
- remote sensing
- Arctic and Antarctic ice dynamics
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
