Sea level prediction using Gaussian Process Models

Sea level prediction is a complicated spatial temporal regression problem that draw a lot of attention these days. Understanding sea level behavior can help us know more about climate change and consequent effects.  However, predicting sea level is not really easy, when we have to deal with many problems like noisy obeservations, censored data, and so on. In this project, we focus on Gaussian process (GPs)  for modelling sea level because of its flexibility and effectiveness.

At the first stage, we are working on how to predict sea level using uncertain inputs with ordering  constraints. One of the inputs to predict the sea level is the information of ages, however due to the limitation of C14 dating technique, we can not obtain true ages of the records, but a noisy version of them. In addition, the true inputs must be in decreasing order. Utilizing this information, we propose a fast and accurate method to estimate the true inputs, and hyper-parameters in GP models.

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