International Journal of Marine Science, 2024, Vol.14, No.3, 193-203 http://www.aquapublisher.com/index.php/ijms 196 2023). These models leverage large datasets from ocean observations and numerical simulations to train predictive algorithms, which can outperform traditional statistical models in terms of accuracy and computational efficiency (Ali et al., 2021). AI is also used for identifying and forecasting ocean phenomena like internal waves, El Niño-Southern Oscillation (ENSO), and sea ice, demonstrating its versatility and potential in marine science (Dong et al., 2022; Song et al., 2023). 4.3 Ensemble forecasting methods Ensemble forecasting methods involve generating multiple forecasts using different models or varying initial conditions to account for uncertainties in predictions. This approach is particularly useful in marine prediction, where the inherent variability of oceanic processes can lead to significant forecast errors. Ensemble methods have been applied to predict surface chloride concentration in marine concrete, demonstrating improved accuracy over standalone models (Cai et al., 2020). Additionally, ensemble forecasting is used to manage the gap between observation and model resolution, allowing for better prediction of constrained and unconstrained ocean features (Jacobs et al., 2021). By combining predictions from various models, ensemble methods provide a more robust and reliable forecast, essential for operational marine forecasting. 4.4 Real-time prediction systems Real-time prediction systems are critical for providing timely and accurate forecasts of marine conditions, which are essential for navigation, fisheries, and disaster management. These systems integrate real-time observational data with numerical models and machine learning algorithms to deliver up-to-date predictions. For example, a phase-resolving wave-forecasting algorithm that assimilates marine radar data has been developed to provide real-time wave forecasts, demonstrating the potential of integrating observational data with numerical models for immediate applications (Simpson, 2020). Similarly, real-time SST forecasts have been enhanced by combining deep learning neural networks with traditional numerical estimators, offering precise location-specific predictions (Sarkar et al., 2020). The continuous advancement of real-time prediction systems is vital for improving the safety and efficiency of marine operations. 5 Applications of Marine Observation and Prediction 5.1 Climate change studies Marine observation technologies play a crucial role in understanding and predicting climate change impacts on marine environments. Advances in remote sensing, environmental DNA (eDNA) assessments, and animal telemetry provide valuable data on ocean conditions and biodiversity, which are essential for climate models and predictions (Capotondi et al., 2019; Ruhl et al., 2021). These technologies help monitor changes in sea level, temperature, and ocean currents, which are influenced by climate variability and change (Capotondi et al., 2019). Additionally, artificial intelligence (AI) algorithms are increasingly used to analyze large datasets, improving the accuracy of climate predictions and identifying phenomena such as El Niño-Southern Oscillation (ENSO) and heatwaves (Song et al., 2023). 5.2 Marine resource management Effective marine resource management relies on accurate and timely data on marine ecosystems. Innovations in monitoring systems, such as molecular approaches (e.g., qPCR, metabarcoding), optical sensing, and acoustic methods, enhance the ability to assess marine biodiversity and environmental status. These technologies support the implementation of frameworks like the Marine Strategy Framework Directive (MSFD) by providing cost-effective and efficient monitoring solutions. AI models also contribute to resource management by predicting ocean components and optimizing the use of marine resources (Jiang et al., 2022; Song et al., 2023). 5.3 Disaster prevention and mitigation Marine observation and prediction technologies are vital for disaster prevention and mitigation. Satellite remote sensing and in situ monitoring instruments provide real-time data on ocean conditions, which are crucial for forecasting and responding to natural disasters such as tsunamis, hurricanes, and storm surges (Capotondi et al., 2019; Ruhl et al., 2021). AI algorithms enhance the predictive capabilities of these systems, allowing for more
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