Péter Tanos

Classification into homogeneous groups using combined cluster and discriminant analysis

Abstract The classification of observations into groups is a general procedure in modern research. However, when searching for homogeneous groups the difficulty of deciding whether further division of a classification is necessary or not to obtain the desired homogeneous groups arises. The presented method, Combined cluster and discriminant analysis (CCDA), aims to facilitate this decision. CCDA consists of three main steps: (I) a basic grouping procedure; (II) a core cycle where the goodness of preconceived and random classifications is determined; and (III) an evaluation step where a decision has to be made regarding division into sub-groups. These steps of the proposed method were implemented in R in a package, under the name of ccda. To present the applicability of the method, a case study on the water quality samples of Neusiedler See is presented, in which CCDA classified the 33 original sampling locations into 17 homogeneous groups, which could provide a starting point for a later recalibration of the lakes monitoring network.

Analysis of Water Quality Data for Scientists

The most often used models are deterministic, although they are prepared from one sampling event. It must be stated that the statistics and model results obtained from this sampling event can significantly change if the sampling is to be reproduced because their results are probability variables (Kovacs & Szekely, 2006). In the case of deterministic models this problem is solved by means of sensitivity analyses, thus the uncertainty in the applied model remains. This may be the reason why the following can be found in the international literature regarding this question: “The future is stochastic modeling” (Kovacs & Szanyi, 2005; Wilkinson, 2006).

Spatial Optimization of Monitoring Networkson the Examples of a River, a Lake-Wetland System and a Sub-Surface Water System

Monitoring systems in general have to meet numerous requirements, the most important of which are representativeness and cost efficiency. The aim of the study, therefore, was to present the spatial optimization of the monitoring networks of a river (the Danube), a wetland-lake system (Kis-Balaton & Lake Balaton), and a sub-surface water system in the watershed of Lake Neusiedl/Fertő over a period of approximately two decades using a novel method, Combined cluster and discriminant analysis (CCDA). In the case of the river the results show that the monitoring network yields redundant information on certain sections, so that of 12 sampling sites 3 can be discarded. It was not, however, enough to consider just the tributaries when it comes to optimization. In the case of the wetland (Kis-Balaton) one pair of sampling sites out of 12, while in the case of Lake Balaton 5 out of 10 can be abandoned. For the sub-surface water system, however, all the 50 sites contained exclusive information; hence, all of these were shown to be necessary. In addition, neighboring sampling sites were compared pairwise using CCDA and the corresponding results were visualized in diagrams or so called “difference maps” indicating the location of the biggest differences. This approach also indicates the researcher where to place new sampling sites should the possibility arise. The discussed methodology proved to be highly useful in the optimization of the monitoring networks of the presented water systems.