  Earthquake prediction is one of the most pursued problems in geoscience.
Different geological and seismological approaches exist for the prediction of
the earthquake and its subsequent land change. However, in many cases, they
fail in their mission. In this paper, we address the well-established
earthquake prediction problem by a novel approach. We use a four-dimensional
location-time machine learning scheme to estimate the time of earthquake and
its land change. We present a study for the Ridgecrest, California 2019
earthquake prediction. We show the accuracy of our method is around 14
centimeters for the land change, and around 2 days for the time of the
earthquake, predicted from data more than 3 years before the earthquake.
