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The aim of the present study is the employment of the artificial neural network (ANN) model in the estimation and evaluation of geoid heights. For that reason, a number of control points with known orthometric heights in the region of North Greece were measured by GPS and used to test the presented algorithms. The derived ANN geoid heights are compared to those produced by a wellknown conventional method through a combined gravimetric geoid. In order to evaluate the computed heights, numerical tests were carried out for points distributed inside and at the borders of the study area. The obtained results show that the ANN model is a competitive approach with certain advantages.

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This study investigates the ionospheric total electron content (TEC) variations prior to the earthquake (MW = 6.9) of 24 May 2014 in Samothraki island of north Aegean Sea in Greece. TEC estimates were analyzed using data from GNSS (GPS+Glonass) permanent networks with the aim to detect possible ionospheric anomalies associate with the seismic event. The test period covers one week of data, 4 days before and two days after the event. Selected GNSS stations are scattered around seismic epicenter of distances from 16 up to 1375 km. TEC values estimated for every hour using PPP technique with Bernese GPS software. A comparison with global TEC estimates derived from CODE and JPL institute confirms the validation of results. It is found that a significant decrease 1-day prior to earthquake occurs at all of the selected stations. This result is not obvious when standard ionospheric model is performed for the estimation of TEC. Therefore, in such cases the use of dedicated GNSS processing data scenario is mandatory. A spatial analysis on TEC estimates with geometrical properties shows that the 1-day decrement is related with the EQ shock and may point the location area of the Earthquake. Finally, we conclude that the lithosphere-atmosphere-ionosphere coupling (LAIC) mechanism through acoustic or gravity waves has a key role for this phenomenology.