As we celebrate #WorldOceanDay, it’s imperative to recognize the profound significance of AI exploring ocean mysteries in sustaining life on Earth. The ocean covers over 70% of our planet’s surface, regulates our climate, and is a vital source of food and livelihood for millions. Moreover, it thrives immense biodiversity, with countless species yet to be discovered. The ocean is not just a vast expanse of water; Phytoplankton, tiny marine plants near the ocean’s surface, produce more than half of the world’s oxygen.
However, despite its critical importance, the ocean remains largely uncharted and mysterious. Exploring its depths presents a monumental challenge. The ocean’s vastness, extreme pressure, and darkness create formidable barriers. Human divers can only descend to limited depths, and conventional underwater vehicles struggle to navigate the complicated terrain and withstand the immense pressure of the abyssal depths. As a result, much of the ocean’s secrets remain shrouded in darkness, waiting to be unveiled. AI exploring ocean mysteries as a tool proposes huge potential to make the analysis easier [1].
Role of AI in Oceanography: Unveiling Mysteries
The use of AI in oceanography study ranges, including the Role of AI in Oceanography, ranges from oceanic phenomena identification to forecasting, and analysis. The enhancing capacity of AI is opening new dimensions for exploring the unexplored ocean. The responsible AI has been majorly use in studying phenomena such as:
● Eddy detection: Mesoscale eddies play important roles in global energy, matter transport, as well as the distribution of nutrients and phytoplankton in the ocean. High rotational speed and accompanying strong shear make eddies highly nonlinear.
Fig. source [1]: Ocean mesoscale eddies identified by a traditional method (VG) and three AI-based algorithms [2]
● Internal waves: Internal waves play an important role in transferring the energies of mesoscale, ocean acoustics, offshore engineering, and submarine navigation. AI-based ocean wave amplitude inversion model using laboratory experiments and satellite in situ observation data enhanced the study of internal waves [3].
Fig source [1]: Internal wave amplitude inversion in the Anderman Sea [3]
● Oil spills: Marine oil spills endanger the marine ecological environment, fisheries, wildlife, and tourism [4]. AI-based image processing models can be utilized to recognize oil spills [5]. Early detection helps to limit the impact of oil spills.
Fig source [1]: Classification of oil spills [5]
● Wave height prediction: Marine renewable energy, maritime transport, and other nearshore, coastal, and offshore applications are dependent upon wave height forecasting. Inspiring fromLSTM as a benchmark technique for understanding sequential data, [6] uses convLSTMto forecast significant wave heights in the eastern and southern waters of China with minimal computational expense.
Fig Source [1]: Wave height prediction during the typhoon period [6]
● Ocean Turbulence Parameterization: The understanding of oceanic phenomenon is highly interconnected to small- and large-scale processes. The AI-based data-driven turbulent mixing parameterization scheme enhances the simulation of the vertical heat flux of the upper ocean. Hence, improves the temperature simulation results of the Tropical Pacific Ocean [7].
Fig Source [1]: Improved temperature simulations in ocean-only modeling when NN-based parameterization[7]
AI’s Impact on Ocean Exploration and Conservation
As technology continues to advance, we can expect AI-driven innovations to unlock new frontiers in marine science and conservation. AI holds the key to unraveling the mysteries of the ocean and harnessing its vast resources sustainably. Key ways AI is enabling ocean exploration:
● Autonomous Underwater Vehicles (AUVs): AI-powered AUVs can operate autonomously to collect data 24/7, without needing to surface. Robots embed with AI algorithms are capable to map the ocean floor and take readings in dangerous, unexplored areas. NASA is also working dedicatedly on robotic navigation tech to explore the deep ocean mysteries [11].
Fig Source [9]: Crabster CR200 developed by KIOST
● Identifying species: AI is capable of processing large amounts of data. AI can rapidly identify and track marine life, helping to catalog the estimated 91% of species that remain undiscovered [8]. Initiatives like, @FathomNet (https://fathomnet.org) help research scientists all over the world to contribute and share insights.
● Marine Behavioral Ecology: Sensors embedded with AI models have extended the capacity of researchers. The continuous data collection and analysis helps to cater to finer insights of different species.
Conclusion
By harnessing the power of AI, we can illuminate the depths of the ocean, not only enriching our scientific understanding but also paving the way for a healthier, more resilient planet. AI Exploring Ocean Mysteries and Role of AI in Oceanography offer promising avenues for research and discovery in this vast realm. At @SmartSoC Solutions, we understand the responsible use of AI for our environment. We have also discussed the potential of AI for preserving biodiversity in our previous blog [10]. Do check out that blog for more insights regarding the role of AI in biodiversity. As we commemorate World Ocean Day, let us embrace the transformative potential of AI and embark on a journey to awaken the depths, forging a brighter future for generations to come.
References:
[1] Dong, Changming, et al. “Recent developments in artificial intelligence in oceanography.” Ocean- Land-Atmosphere Research (2022).
[2] Xu, Guangjun, et al. “Application of three deep learning schemes into oceanic eddy detection.” Frontiers in Marine Science 8 (2021): 672334.
[3] Zhang, Xudong, et al. “Oceanic internal wave amplitude retrieval from satellite images based on data-driven transfer learning model.” Remote Sensing of Environment 272 (2022): 112940. [4] Zhang, Guang J., Xiaoliang Song, and Yong Wang. “The double ITCZ syndrome in GCMs: A coupled feedback problem among convection, clouds, atmospheric and ocean circulations.” Atmospheric Research 229 (2019): 255-268.
[5] Yekeen, Shamsudeen Temitope, Abdul-Lateef Balogun, and Khamaruzaman B. Wan Yusof. “A novel deep learning instance segmentation model for automated marine oil spill detection.” ISPRS Journal of Photogrammetry and Remote Sensing 167 (2020): 190-200.
[6] Zhou, Shuyi, et al. “Convlstm-based wave forecasts in the south and east China seas.” Frontiers inMarine Science 8 (2021): 680079.
[7] Zhu, Yuchao, et al. “Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations.” National Science Review 9.8 (2022): nwac044.
[8] https://swisscognitive.ch/2024/05/14/how-ai-enables-ocean-exploration/
[9] https://spectrum.ieee.org/six-legged-underwater-robot-crabster
[11] https://www.nasa.gov/centers-and-facilities/jpl/robotic-navigation-tech-will-explore-the-deep-ocean/