International Journal of Marine Science, 2024, Vol.14, No.3, 193-203 http://www.aquapublisher.com/index.php/ijms 201 integration of AI with traditional physical models is also a growing trend, offering new ways to improve the accuracy and efficiency of marine predictions (Song et al., 2023). Additionally, new holistic observational approaches, inspired by initiatives like Tara Oceans, are being developed to support and expand ecosystem modeling and forecasting by bridging global and local observations (Capotondi et al., 2019). These innovations hold promise for addressing current challenges and advancing the field of marine science. 8 Concluding Remarks The development and application of key technologies in marine observation and prediction have significantly advanced our understanding of oceanographic processes and improved the accuracy of marine forecasts. Drifting buoys, part of the Argo program, have provided invaluable data on ocean temperature and salinity profiles, significantly impacting weather forecasts and climate models. Satellite remote sensing, particularly through instruments like MODIS, has enabled extensive monitoring of sea surface temperatures, chlorophyll concentrations, and ocean currents, playing a crucial role in tracking large-scale oceanographic phenomena. High-Frequency Radar (HFR) systems have been effective in measuring coastal surface currents. Autonomous Underwater Vehicles (AUVs) have revolutionized seafloor mapping and deep-sea exploration, providing high-resolution geological and biological data. Ocean gliders has enhanced the monitoring of water column properties and current velocities, contributing to climate variability predictions and navigation support. The rapid advancements in marine observation technologies underscore the importance of continued innovation and research. As environmental challenges and climate change impacts intensify, there is an increasing need for precise and comprehensive marine data. The integration of artificial intelligence (AI) and machine learning (ML) into marine observation and prediction systems holds great potential to enhance data analysis capabilities and improve the accuracy of ocean forecasts. However, the challenges of data volume, analytical capacity, and model accuracy necessitate ongoing research and development. Interdisciplinary observational networks and improved understanding of oceanographic processes are critical to addressing these challenges and advancing the field. To maximize the potential of marine observation technologies, several recommendations are proposed. Firstly, enhancing data integration and interoperability by developing standardized protocols for data collection, processing, and sharing across different technologies and platforms, as well as fostering collaborations between international marine observation programs to create a unified global observation network. Meanwhile, investing in AI and ML technologies by focusing on developing algorithms tailored to marine data analysis and prediction and integrating AI with traditional physical models to enhance the accuracy and efficiency of marine forecasts. Additionally, expanding observational networks by increasing the deployment of drifting buoys, gliders, and HFR systems to improve spatial and temporal coverage of oceanographic data and establishing more comprehensive observational networks in underrepresented regions, particularly in the Southern Hemisphere and polar areas. Lastly, addressing knowledge gaps through targeted research by conducting focused studies on the physical and biological processes that influence marine ecosystems to improve model formulations and studying the impacts of climate change on ocean conditions to enhance predictive capabilities. Continued innovation, research, and collaboration are essential to advancing marine observation technologies and improving our understanding and management of the oceans. These efforts will ultimately contribute to better environmental stewardship and more effective responses to global marine challenges. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Ali A., Fathalla A., Salah A., Bekhit M., and Eldesouky E., 2021, Marine data prediction: an evaluation of machine learning deep learning and statistical predictive models, Computational Intelligence and Neuroscience, 2021(1): 8551167. https://doi.org/10.1155/2021/8551167 Aniceto A., Pedersen G., Primicerio R., Biuw M., Lindstrøm U., and Camus L., 2020, Arctic marine data collection using oceanic gliders: providing ecological context to cetacean vocalizations, Frontiers in Marine Science, 7: 585754. https://doi.org/10.3389/fmars.2020.585754
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