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The paper presents a comparison of several empirical models used to determine cation exchange capacity (CEC) and base saturation (BS). CEC and BS determinations in mineral soils in southern Poland have been used in comparisons of individual models. The soils represented different valuation classes and differed in their typology. The following models were used: multiple regression, polynomial neural network and fuzzy-neural network (ANFIS). Models used for comparative purposes represent pedotransfer functions (PTF), developed for various climate conditions using various analytic methods. The processed results were compared with the modelling results based on observational data analysis. Relatively low applicability has been found for models based on data for other climate conditions and other analytical methods in terms of accurate CEC determination.
Various ways of approaching the horizontal distribution trend (tendency) of Chromium (Cr) in soil, where pollution by this element is high, were analyzed. Interpolation algorithms: triangular irregular network (TIN), kriging, regularized spline with tension (RST), and artificial neural networks; radial basis function network (RBF), probabilistic neural network (PNN), generalized regression neural network (GRNN) and mixture density network (MDN) were applied. Data from field experiments, carried out in the area of the chemical plant in Alwernia, were used. The soil pollution spatial distribution examinations lead to the conclusion that in the first place was the information precision determination, and also the limit of error, through the pollution evaluation acceptance, whereas in the second place was the indication or standing out the regularity connected with the emission effect mechanism. It seems that the chromium concentration in soils variation, noticed even on short distances, makes the acceptance of interpolation method difficult, as a method of contamination distribution evaluation. On the other hand the considerable nonlinearity makes difficult the acceptance of regression model. In these circumstances, the possibility which is worth consideration is the modelling with the application of neuron networks, that is also hybrid solution application (for instance MDN), which gives the possibility of Cr concentration in soil variation deeper analysis (e. g. calculation local probability distribution, local variance, etc.).
Significant spatial variability of the accumulation of pollutants in soils can make problems in the determination of the borders defining a zone where pollution, according to the applied legal requirements, is excessive. Particular difficulty is caused by a short-distance variability, disturbing the regularity in a spatial distribution of pollution around the source of emission. The paper presents an alternative, compared to traditional interpolation methods, algorithms based on the optimization and the application non-linear neural networks called mixture density network MDN and feature space mapping network FSM.The benefit from the application of this approach is more information referring to the distribution of pollution. This approach allows the estimation of the local variance of the accumulation of pollutants and approximate local distribution. This allows greater extent of taking into account the uncertainty connected with the spatial variability of soil pollution.
Opening new mines, deeper operations and the constant spread of mining activity to new terrain is associated with more or less extensive disturbance of natural water regimes. This is manifested in the drying of farm wells and ponds, reduced flow in water courses and, sometimes even their complete disappearance with extreme drying of orogen and soils. Typical symptoms of such disturbances are hydrological and soil changes and alteration of habitats. A very large disturbance of this kind is subsidence depression zone surrounding the Bełchatów brown coal strip mine. The authors discuss its effects on soil and habitat conditions, using two forest districts as examples.
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