DOI: 10.5593/sgem2017H/33/S12.035


L. Asimopolos, N. S. Asimopolos
Thursday 23 November 2017 by Libadmin2017

References: 17th International Multidisciplinary Scientific GeoConference SGEM 2017, www.sgemviennagreen.org, SGEM2017 Vienna GREEN Conference Proceedings, ISBN 978-619-7408-27-0 / ISSN 1314-2704, 27 - 29 November, 2017, Vol. 17, Issue 33, 283-290 pp; DOI: 10.5593/sgem2017H/33/S12.035


In this paper we have developed a set of multi-parametric data analysis programs, which can be used in hydrogeological studies.
Considering that in these studies we use large sets of multi-parametric data with spatial and temporal distribution we focused on the analysis with polynomial tendencies surfaces and hypersurfaces, analysis by moving average with different dimensions of windows and analysis of the variance of the correlation factor with mobile window.
The first part of the paper is dedicated to the description of the algorithms and realization/implementation of this programs.
The generalization of the tendency surfaces to the trend hypersurfaces, adds more information in situations where we use more than two independent variables and a dependent variable.
In this way, we can obtain the analytical expressions of each surface or hypersurface, of different degrees, which show the evolution of a parameter according to the other parameters considered independent.
Moving average with different dimensions windows is also a very useful tool in studying properties and hydrogeological cycles. Correlations between polynomial surfaces and moving average give us information about separating of the local causes from the regional or global causes of the studied parameters.
To study the variation of the correlation factor between two parameters, we have developed a program that allows the use of different windows, both for the study of spatial variation and temporal variation.
These methods of analysis were compared with the spectral analysis using the Fast Fourier Transform 2D/3D and the wavelet and multi-resolution analysis.

Keywords: Polynomial tendencies surfaces, moving average, correlation factor