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Presented work reports on the use of artificial neural networks to recognize and classify water reservoir types (lakes, rivers) and the nature of their surroundings (forests, fields, meadows) based on the chemical composition of sediments. The quantitative content of a selection of elements (Ag, As, Ba Ca, Cd, Co, Cr, Cu, Fe, Hg, Mg, Mn, Ni, P, Pb, S, Sr, TOC – Total Organic Cabon, V and Zn) in the sediments of lakes and rivers in the Lublin Province (Poland) were taken and used as working data file. Statistical analysis suggested that both reservoir types and area usage differ in terms of the quantity of studied determinants (elements) and thus might be distinguished on their basis. Artificial neural networks were then examined with respect to their ability to recognize and classify the data. Multilayer perceptron was used as the statistical model. Constructed models were able to give correct answers in 74% of cases when classifying reservoir’s area usage and 100% for the type of body of water
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