Emerging Computational Intelligence Techniques to Address Challenges in Oceanic Computing

This research topic focuses on the transformative impact of Emerging Computational Intelligence (CI) Techniques in addressing the challenges within Oceanic Computing. Oceanic computing is crucial in efficiently utilizing marine resources, comprehending ecosystem dynamics, and promoting sustainable maritime operations. The historically immense computational requirements needed for analyzing intricate marine data have hindered research advancement. Currently, CI technologies are pioneering new approaches in various ocean-related fields, including fluid dynamics, marine geology, ocean energy, and the operation of autonomous underwater vehicles (AUVs) and unmanned surface vehicles (USVs). CI's advanced modeling and predictive capabilities are essential for accurately forecasting maritime weather and enhancing navigational safety through a better understanding of ocean currents and wave patterns. In marine geology, CI tools facilitate detailed analysis of seabed data, which is crucial for discovering geological structures and evaluating mineral resources, thereby revolutionizing the prospects of underwater exploration and resource extraction. Ocean energy research has also transformed, with CI-enhanced methodologies optimizing the harnessing and converting oceanic forces into renewable energy, providing new energy alternatives for coastal areas. Using CI in unmanned maritime vehicles enhances the scope and efficiency of oceanographic research and environmental surveillance, improving safety and operational efficiency. Furthermore, CI plays a vital role in advancing underwater robotics, which supports various activities from environmental preservation to archaeological explorations and energy harvesting. This Research Topic aims to explore how CI is reshaping our interaction with and understanding of oceanic environments, focusing on enhancing knowledge, monitoring, conservation, and sustainable utilization of ocean resources.

Despite significant advances in oceanic computing, maritime operations face persistent challenges related to data complexity, dynamic environmental conditions, and the need for sustainable practices. The vast and heterogeneous nature of marine data, coupled with the unpredictable behaviors of oceanic environments, makes traditional computational methods inadequate. This limitation impacts various critical areas, such as real-time decision-making in navigation, effective environmental monitoring, and optimization of renewable energy production from oceanic resources. Recent advancements in CI have shown promise in modeling complex spatial and temporal patterns in oceanic datasets and offered new solutions to simulate and optimize complex oceanic environments for better performance. Additionally, breakthroughs in sensor technology and data fusion methods provide richer data inputs, enhancing the training and performance of CII models.

It calls for original and novel papers related, but not limited to the following topics:

• Leveraging emerging CI techniques, predictive modeling becomes vital for understanding and managing oceanic environmental dynamics.
• Predictive analytics for decarbonizing maritime transport.
• The role and efficiency of CI techniques in multimodal learning for oceanic computing
• CI-driven monitoring of oceanic pathogens
• CI in evaluating cumulative impacts of human activities on marine ecosystems
• CI-based solutions for coastal habitat protection and restoration

Please note that papers without ocean management applications will not be considered.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Marine science, Computational intelligence, Machine learning, Maritime vessels

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.