  We investigate the accuracy of conventional machine learning aided algorithms
for the prediction of lateral land movement in an area using the precise
position time series of permanent GNSS stations. The machine learning
algorithms that are used are tantamount to the ones used in [1], except for the
radial basis functions, i.e. multilayer perceptron, Bayesian neural network,
Gaussian processes, k-nearest neighbor, generalized regression neural network,
classification and regression trees, and support vector regression. A
comparative analysis is presented in which the accuracy level of the mentioned
machine learning methods is checked against each other. It is shown that the
most accurate method for both of the components of the time series is the
Gaussian processes, achieving up to 9.5 centimeters in accuracy.
