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Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models.

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This study compared statistical, machine learning, and deep learning models for predicting sea surface temperature (SST) and significant wave height (SWH). Deep learning models showed slightly better performance than machine learning, with both outperforming statistical models.

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Area of Science:

  • Marine science and technology
  • Data science and artificial intelligence
  • Oceanography

Background:

  • Advancements in ocean observation technology generate vast amounts of marine data.
  • Predicting marine data like sea surface temperature (SST) and significant wave height (SWH) is crucial for various applications.
  • Existing literature categorizes marine data forecasting into statistical, machine learning, and deep learning approaches.

Purpose of the Study:

  • To compare the performance of statistical, machine learning, and deep learning models for predicting SST and SWH.
  • To evaluate these models using a real-world dataset from the Korea Hydrographic and Oceanographic Agency.
  • To provide insights into the effectiveness of different predictive modeling techniques for marine data.

Main Methods:

  • Implementation of statistical, machine learning, and deep learning models.
  • Utilizing a real dataset of SST and SWH from the Korea Hydrographic and Oceanographic Agency.
  • Comparative analysis of model performance using four distinct evaluation metrics.

Main Results:

  • Deep learning models demonstrated slightly superior overall performance compared to machine learning models.
  • Both deep learning and machine learning approaches significantly outperformed traditional statistical models.
  • The study provides empirical evidence on the relative strengths of these predictive methods for marine data.

Conclusions:

  • Deep learning and machine learning models offer more effective solutions for predicting SST and SWH than statistical methods.
  • The findings highlight the potential of advanced AI techniques in enhancing marine data analysis and prediction.
  • This comparative study serves as a benchmark for future research in marine data forecasting.