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This paper introduces the alternating conditional expectation (ACE) algorithm of Breiman and Friedman (1985) in multiple regression problems in groundwater monitoring data analysis. This special inverse nonparametric approach can be applied easily for estimating the optimal transformations of different groundwater monitoring data from the Bükk Mountains to obtain maximum correlation between observed aquifer variables. The approach does not require a priori assumptions of a mathematical form, and the optimal transformations are derived solely based on the groundwater data set. The advantages and applicability of the proposed approach to solve different multiple regression problems in hydrogeology or in groundwater management are illustrated by means of case studies from a Hungarian karst aquifer. It is demonstrated that the ACE method has certain advantages in some fitting problems of groundwater science over the traditional multiple regression.In the past, different groundwater monitoring data (like groundwater level, groundwater temperature and conductance, etc.) had been used for groundwater management purposes in the Bükk Mountains. One of the difficulties in earlier approaches has been the need to make some kind of assumption of the expected mathematical forms among the investigated reservoir and petrophysical variables. By using nonparametric regression, the need to assume a specific form of model is avoided, and a clearer vision of the relationships between aquifer parameters can be revealed in the Bükk Mountains, where karst water is the main source of potable water supply. Complex monitoring data from the Bükk Mountains were analyzed using the ACE inverse method, and results were verified successfully against quantitative and qualitative field observations.

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Pesticides, chlorinated organic compounds and hydrocarbons are among the most threatening soil and groundwater contaminants because of their mobility and persistence in the subsurface, their widespread use, and their health effects. Hazardous chemicals getting into underground medium can be especially dangerous because they may remain persistently hidden from human eyes for a long time and their harmful impact on health may appear much later than their emission time and spatially far from the source of contamination. Development and combination of reliable and accurate geophysical methods and hydrogeological transport models with the traditional chemical analytics are greatly needed to assess the risk posed by the contamination plumes of these compounds to the subsurface.The paper presents the successful cooperation of these three disciplines in detailed characterization of subsurface hydrocarbon contaminants in a test site of Hungary. Combining the chemical analysis with high resolution geophysical methods and hydrogeological transport modeling the 4 dimensional characteristics of the contamination can be produced. The interdisciplinary research produced new developments and results in all participating fields of sciences.

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Agrokémia és Talajtan
Katalin Sárdi
P. Csathó
I. Sisák
E. Osztoics
P. Szűcs
, and
Á. Balázsy
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