Gaussian Process Regression Using Spatial-Temporal covariance: Research on Sea Level Prediction
نویسنده
چکیده
It is only recent that researcher have become to realize the importance of sea level prediction. By studying the global and local sea level, we can provide critical information about the relations between the Earth’s climate or atmosphere and our oceans. In this paper, I use Gaussian Process Regression with spatial-temporal covariance for sea level prediction. I conducted experiments on a public dataset, including global sea levels of more than 20 years. The predictive model can precisely predict the trend of sea level changes. Besides, the most interesting thing is that this model correctly discover the EI Nino event in 1997-’98. In the end of this paper, I also discussed the influence of mean functions and covariance functions while using Gaussian Process Regression.
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