How AI Can Improve Ocean Current Predictions
Scientists use GPS-tagged buoys to study ocean currents and record their velocities. These buoys provide valuable data for understanding the movement of water in the ocean. However, current models used to analyze this data often make unrealistic assumptions about water behavior, leading to inaccurate predictions.
A new study by a research team including computer scientists from MIT and oceanographers has developed a model that incorporates machine learning and fluid dynamics to make more accurate predictions about ocean currents. This model outperforms traditional statistical models and can identify divergences, where water rises or sinks below the surface.
This new model has important applications in various fields. It can help scientists forecast weather more precisely, estimate the spread of oil after spills, and measure energy transfer in the ocean. Additionally, it enables oceanographers to monitor the transportation of biomass, carbon, plastics, and nutrients in the ocean, which is crucial for understanding climate change.
The researchers evaluated their model using synthetic and real ocean buoy data. In both cases, the new model demonstrated superior performance compared to traditional models and another machine-learning approach. It accurately predicted currents and identified divergences with higher accuracy.
Moving forward, the research team aims to incorporate a time element into their model to account for temporal variations in currents. They also plan to improve their model’s ability to handle noise in the data caused by external factors like wind.
By integrating fluid dynamics into their model and using AI, the researchers have developed a more accurate and reliable method for predicting ocean currents. This advancement has significant implications for weather forecasting, environmental monitoring, and climate change research.
Overall, the study highlights the importance of incorporating physics knowledge into AI models to enhance their predictive capabilities. With further development, this new model has the potential to revolutionize our understanding of ocean currents and their impact on the environment.